Methods and systems for data collection, learning, and streaming of machine signals for analytics and maintenance using the industrial internet of things

ABSTRACT

A system for predicting a service event from vibration data generally includes an industrial machine comprising at least one vibration sensor disposed to capture vibration of a portion of the industrial machine; a vibration analysis circuit in communication with the at least one vibration sensor; a multi-segment vibration frequency spectra structure that facilitates mapping the captured vibration to one vibration frequency segment of a multi-segment vibration frequency; a severity unit algorithm that receives the frequency of the captured vibration and the corresponding vibration frequency segment and produces a severity value which is then mapped to one of a plurality of severity units defined for the corresponding vibration frequency segment; and a signal generating circuit that receives the one of the plurality of severity units, and based thereon, signals a predictive maintenance server to execute a corresponding maintenance action on the portion of the industrial machine.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a bypass continuation of International ApplicationNumber PCT/US2019/020044, filed Feb. 28, 2019, entitled METHODS ANDSYSTEMS FOR DATA COLLECTION, LEARNING, AND STREAMING OF MACHINE SIGNALSFOR ANALYTICS AND MAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS,which claims priority to U.S. Provisional Patent Application Ser. No.62/714,078 filed Aug. 2, 2018, entitled METHODS AND SYSTEMS FORSTREAMING OF MACHINE SIGNALS FOR ANALYTICS AND MAINTENANCE USING THEINDUSTRIAL INTERNET OF THINGS; U.S. Provisional Patent Application Ser.No. 62/713,897 filed Aug. 2, 2018, entitled METHODS AND SYSTEMS FOR DATACOLLECTION AND LEARNING USING THE INDUSTRIAL INTERNET OF THINGS; U.S.Provisional Patent Application Ser. No. 62/757,166 filed Nov. 8, 2018,entitled METHODS AND SYSTEMS FOR STREAMING OF MACHINE SIGNALS FORANALYTICS AND MAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS; U.S.Provisional Patent Application Ser. No. 62/799,732 filed Jan. 31, 2019,entitled METHODS AND SYSTEMS FOR DATA COLLECTION, LEARNING, ANDSTREAMING OF MACHINE SIGNALS FOR ANALYTICS AND MAINTENANCE USING THEINDUSTRIAL INTERNET OF THINGS; U.S. Non-Provisional patent applicationSer. No. 16/143,286 filed Sep. 26, 2018, entitled METHODS AND SYSTEMSFOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTIONENVIRONMENT WITH FREQUENCY BAND ADJUSTMENTS FOR DIAGNOSING OIL AND GASPRODUCTION EQUIPMENT; and U.S. Non-Provisional patent application Ser.No. 15/973,406 filed May 7, 2018, entitled METHODS AND SYSTEMS FORDETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTIONENVIRONMENT WITH LARGE DATA SETS.

U.S. Non-Provisional patent application Ser. No. 16/143,286 filed Sep.26, 2018, entitled METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIALINTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH FREQUENCY BANDADJUSTMENTS FOR DIAGNOSING OIL AND GAS PRODUCTION EQUIPMENT is a bypasscontinuation of International Application Number PCT/US2018/045036,filed Aug. 2, 2018, entitled METHODS AND SYSTEMS FOR DETECTION IN ANINDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH LARGEDATA SETS, published on Feb. 7, 2019, as WO/2019/028269, which claimspriority to U.S. Non-Provisional patent application Ser. No. 15/973,406,filed May 7, 2018, entitled METHODS AND SYSTEMS FOR DETECTION IN ANINDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH LARGEDATA SETS, which is a bypass continuation-in-part of InternationalApplication Number PCT/US2017/031721, filed May 9, 2017, entitledMETHODS AND SYSTEM FOR THE INDUSTRIAL INTERNET OF THINGS, published onNov. 16, 2017, as WO/2017/196821, and which claims priority to at leastU.S. Provisional Patent Application Ser. No. 62/333,589, filed May 9,2016, entitled STRONG FORCE INDUSTRIAL IOT MATRIX; U.S. ProvisionalPatent Application Ser. No. 62/350,672, filed Jun. 15, 2016, entitledSTRATEGY FOR HIGH SAMPLING RATE DIGITAL RECORDING OF MEASUREMENTWAVEFORM DATA AS PART OF AN AUTOMATED SEQUENTIAL LIST THAT STREAMSLONG-DURATION AND GAP-FREE WAVEFORM DATA TO STORAGE FOR MORE FLEXIBLEPOST-PROCESSING; U.S. Provisional Patent Application Ser. No.62/412,843, filed Oct. 26, 2016, entitled METHODS AND SYSTEMS FOR THEINDUSTRIAL INTERNET OF THINGS; and U.S. Provisional Patent ApplicationSer. No. 62/427,141, filed Nov. 28, 2016, entitled METHODS AND SYSTEMSFOR THE INDUSTRIAL INTERNET OF THINGS, and in which InternationalApplication Number PCT/US2018/045036 and U.S. Ser. No. 15/973,406 alsoclaim priority to U.S. Provisional Patent Application Ser. No.62/540,557, filed Aug. 2, 2017, entitled SMART HEATING SYSTEMS IN ANINDUSTRIAL INTERNET OF THINGS; U.S. Provisional Patent Application Ser.No. 62/562,487, filed Sep. 24, 2017, entitled METHODS AND SYSTEMS FORTHE INDUSTRIAL INTERNET OF THINGS; and U.S. Provisional PatentApplication Ser. No. 62/583,487, filed Nov. 8, 2017, entitled METHODSAND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS, and U.S. ProvisionalPatent Application Ser. No. 62/540,513, filed Aug. 2, 2017, entitledSYSTEMS AND METHODS FOR SMART HEATING SYSTEM THAT PRODUCES AND USESHYDROGEN FUEL. This application also claims priority to U.S. ProvisionalPatent Application Ser. No. 62/713,897, filed Aug. 2, 2018, entitledMETHODS AND SYSTEMS FOR DATA COLLECTION AND LEARNING USING THEINDUSTRIAL INTERNET OF THINGS, and to U.S. Provisional PatentApplication Ser. No. 62/757,166, filed Nov. 2, 2018, entitled METHODSAND SYSTEMS FOR STREAMING OF MACHINE SIGNALS FOR ANALYTICS ANDMAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS, which are each abypass continuation-in-part of International Application NumberPCT/US2017/031721, filed: May 9, 2017, entitled METHODS AND SYSTEM FORTHE INDUSTRIAL INTERNET OF THINGS, published on Nov. 16, 2017, asWO/2017/196821, and which claims priority to U.S. Provisional PatentApplication Ser. No. 62/333,589, filed May 9, 2016, entitled STRONGFORCE INDUSTRIAL IOT MATRIX; U.S. Provisional Patent Application Ser.No. 62/350,672, filed Jun. 15, 2016, entitled STRATEGY FOR HIGH SAMPLINGRATE DIGITAL RECORDING OF MEASUREMENT WAVEFORM DATA AS PART OF ANAUTOMATED SEQUENTIAL LIST THAT STREAMS LONG-DURATION AND GAP-FREEWAVEFORM DATA TO STORAGE FOR MORE FLEXIBLE POST-PROCESSING; U.S.Provisional Patent Application Ser. No. 62/412,843, filed Oct. 26, 2016,entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS; andU.S. Provisional Patent Application Ser. No. 62/427,141, filed Nov. 28,2016, entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OFTHINGS. This application also claims priority to U.S. Provisional PatentApplication Ser. No. 62/540,557, filed Aug. 2, 2017, entitled SMARTHEATING SYSTEMS IN AN INDUSTRIAL INTERNET OF THINGS; U.S. ProvisionalPatent Application Ser. No. 62/562,487, filed Sep. 24, 2017, entitledMETHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS; and U.S.Provisional Patent Application Ser. No. 62/583,487, filed Nov. 8, 2017,entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS. Theabove applications are each hereby incorporated by reference as if fullyset forth herein in their entirety.

BACKGROUND 1. Field

The present disclosure relates to methods and systems for datacollection in industrial environments, as well as methods and systemsfor leveraging collected data for monitoring, remote control, autonomousaction, and other activities in industrial environments.

2. Description of the Related Art

Heavy industrial environments, such as environments for large scalemanufacturing (such as manufacturing of aircraft, ships, trucks,automobiles, and large industrial machines), energy productionenvironments (such as oil and gas plants, renewable energy environments,and others), energy extraction environments (such as mining, drilling,and the like), construction environments (such as for construction oflarge buildings), and others, involve highly complex machines, devicesand systems and highly complex workflows, in which operators mustaccount for a host of parameters, metrics, and the like in order tooptimize design, development, deployment, and operation of differenttechnologies in order to improve overall results. Historically, data hasbeen collected in heavy industrial environments by human beings usingdedicated data collectors, often recording batches of specific sensordata on media, such as tape or a hard drive, for later analysis. Batchesof data have historically been returned to a central office foranalysis, such as undertaking signal processing or other analysis on thedata collected by various sensors, after which analysis can be used as abasis for diagnosing problems in an environment and/or suggesting waysto improve operations. This work has historically taken place on a timescale of weeks or months, and has been directed to limited data sets.

The emergence of the Internet of Things (IoT) has made it possible toconnect continuously to, and among, a much wider range of devices. Mostsuch devices are consumer devices, such as lights, thermostats, and thelike. More complex industrial environments remain more difficult, as therange of available data is often limited, and the complexity of dealingwith data from multiple sensors makes it much more difficult to produce“smart” solutions that are effective for the industrial sector. A needexists for improved methods and systems for data collection inindustrial environments, as well as for improved methods and systems forusing collected data to provide improved monitoring, control,intelligent diagnosis of problems and intelligent optimization ofoperations in various heavy industrial environments.

Industrial system in various environments have a number of challenges toutilizing data from a multiplicity of sensors. Many industrial systemshave a wide range of computing resources and network capabilities at alocation at a given time, for example as parts of the system areupgraded or replaced on varying time scales, as mobile equipment entersor leaves a location, and due to the capital costs and risks ofupgrading equipment. Additionally, many industrial systems arepositioned in challenging environments, where network connectivity canbe variable, where a number of noise sources such as vibrational noiseand electro-magnetic (EM) noise sources can be significant and in variedlocations, and with portions of the system having high pressure, highnoise, high temperature, and corrosive materials. Many industrialprocesses are subject to high variability in process operatingparameters and non-linear responses to off-nominal operations.Accordingly, sensing requirements for industrial processes can vary withtime, operating stages of a process, age and degradation of equipment,and operating conditions. Previously known industrial processes sufferfrom sensing configurations that are conservative, detecting manyparameters that are not needed during most operations of the industrialsystem, or that accept risk in the process, and do not detect parametersthat are only occasionally utilized in characterizing the system.Further, previously known industrial systems are not flexible toconfiguring sensed parameters rapidly and in real-time, and in managingsystem variance such as intermittent network availability. Industrialsystems often use similar components across systems such as pumps,mixers, tanks, and fans. However, previously known industrial systems donot have a mechanism to leverage data from similar components that maybe used in a different type of process, and/or that may be unavailabledue to competitive concerns. Additionally, previously known industrialsystems do not integrate data from offset systems into the sensor planand execution in real time.

Industrial environments are widely populated with large, complex, heavymachines that are designed to have very long working lifetimes and haveongoing service requirements, including requirements for scheduledmaintenance and for often unanticipated repairs.

Many of the large industrial machines that require ongoing maintenance,service and repairs are involved in high stakes production processes andother processes, such as energy production, manufacturing, mining,drilling, and transportation, that preferably involve minimal or nointerruption. An unanticipated problem, or an extended delay in aservice operation that requires a shutdown of a machine that is criticalto such a process can cost thousands, or even millions of dollars perday. Embodiments disclosed herein, as well as in the documentsincorporated by reference herein, provide for, among many other things,a platform having improved devices, systems, components, processes andmethods for collection, processing, and use of data from and aboutindustrial machines, including for purposes of predicting faults,anticipating needs for maintenance, and facilitating repairs. However,in some areas, the workforce that maintains, services and repairs heavyindustrial machines is aging. As workers retire, much of their expertiseis lost, and new workers often lack even basic factual information abouta machine (such as about the internal structure of the machine),operational information (such as about how it is intended to behave invarious working modes) and/or procedural information (such as how toperform a routine maintenance task), much less the know-how andexpertise to handle a more complex procedure, such as a repair, that mayrequire multi-step procedures that use unfamiliar parts or tools.Another challenge is finding relevant parts and components for anindustrial machine, such as ones that may be required for an emergencyrepair, in a timely manner, so that they are available at the place andtime required for the work. Information about the internal structure,parts or components of a machine may be absent, so that a worker may berequired to guess about what is wrong, what part is involved, and how arepair needs to be conducted. A repair may require multiple visits, suchas one or more to discover the nature of a problem, what parts need tobe replaced, and what tools are required, and one or more others toconduct the repair once the relevant parts and tools arrive. This canmean days of delay at massive cost to the operator of the machinery.This process may repeat a few months or years later, as the next workermay have no way of accessing the knowledge acquired about the internalstructure, parts or components of the machine that was acquired by aninitial worker.

A need exists for improved methods and systems for collecting,discovering, capturing, disseminating, managing, and processinginformation about industrial machines, including factual information(such as about internal structures, parts and components), operationalinformation and procedural information, including know-how and otherinformation relevant to maintenance, service and repairs. A need alsoexists for improved methods and systems for finding a set of workershaving relevant know-how and expertise about maintenance, service andrepair of a particular machine. A need also exists for improved methodsand systems for finding, ordering, and fulfilling orders for relevantparts and components, so that maintenance, service and repair operationscan occur seamlessly, with minimal disruption.

SUMMARY

In embodiments, an industrial machine predictive maintenance system mayinclude an industrial machine data analysis facility that generatesstreams of industrial machine health monitoring data by applying machinelearning to data representative of conditions of portions of industrialmachines received via a data collection network. The system may furtherinclude an industrial machine predictive maintenance facility thatproduces industrial machine service recommendations responsive to thehealth monitoring data by applying machine fault detection andclassification algorithms thereto. The system may further include acomputerized maintenance management system (CMMS) that produces at leastone of orders and requests for service and parts responsive to receivingthe industrial machine service recommendations. And, the system mayinclude a service and delivery coordination facility that receives andprocesses information regarding services performed on industrialmachines responsive to the at least one of orders and requests forservice and parts, thereby validating the services performed whileproducing a ledger of service activity and results for individualindustrial machines.

In embodiments, a method of predicting a service event from vibrationdata may include a set of operational steps including capturingvibration data from at least one vibration sensor disposed to capturevibration of a portion of an industrial machine. The captured vibrationdata may be processed to determine at least one of a frequency,amplitude, and gravitational force of the captured vibration. Next, asegment of a multi-segment vibration frequency spectra that bounds thecaptured vibration may be determined, based on, for example thedetermined frequency. Thus, calculating a vibration severity unit forthe captured vibration may be based on the determined segment and atleast one of the peak amplitudes and the gravitational force derivedfrom the vibration data. Additionally, the method may include generatinga signal in a predictive maintenance circuit for executing a maintenanceaction on the portion of the industrial machine based on the severityunit.

In embodiments, zero-gap signal capture at a streaming sample rate mayinclude sampling a signal at the streaming sample rate, therebyproducing a plurality of samples of the signal. The plurality of samplesof the signal may be allocated with a signal routing circuit thatgenerates a first portion of the plurality of samples of the signal to afirst signal analysis circuit, the portion based on a first signalanalysis sampling rate that is less than the streaming sample rate. Theplurality of samples of the signal may be allocated with a signalrouting circuit that generates a second portion of the plurality ofsamples of the signal to a second signal analysis circuit, the portionbased on a second signal analysis sampling rate that is less than thestreaming sample rate. In embodiments, the zero-gap signal capture mayfurther include storing the plurality of samples of the signal, anoutput of the first signal analysis circuit, and an output of the secondsignal analysis circuit. In embodiments, the allocated first portion andthe second portion of the plurality of samples in the stored pluralityof samples are tagged with indicia that references the correspondingstored signal analysis output.

Methods and systems are provided herein for data collection inindustrial environments, as well as for improved methods and systems forusing collected data to provide improved monitoring, control, andintelligent diagnosis of problems and intelligent optimization ofoperations in various heavy industrial environments. These methods andsystems include methods, systems, components, devices, workflows,services, processes, and the like that are deployed in variousconfigurations and locations, such as: (a) at the “edge” of the Internetof Things, such as in the local environment of a heavy industrialmachine; (b) in data transport networks that move data between localenvironments of heavy industrial machines and other environments, suchas of other machines or of remote controllers, such as enterprises thatown or operate the machines or the facilities in which the machines areoperated; and (c) in locations where facilities are deployed to controlmachines or their environments, such as cloud-computing environments andon-premises computing environments of enterprises that own or controlheavy industrial environments or the machines, devices or systemsdeployed in them. These methods and systems include a range of ways forproviding improved data include a range of methods and systems forproviding improved data collection, as well as methods and systems fordeploying increased intelligence at the edge, in the network, and in thecloud or premises of the controller of an industrial environment.

Methods and systems are disclosed herein for continuous ultrasonicmonitoring, including providing continuous ultrasonic monitoring ofrotating elements and bearings of an energy production facility; forcloud-based systems including machine pattern recognition based on thefusion of remote, analog industrial sensors or machine pattern analysisof state information from multiple analog industrial sensors to provideanticipated state information for an industrial system; for on-devicesensor fusion and data storage for industrial IoT devices, includingon-device sensor fusion and data storage for an Industrial IoT device,where data from multiple sensors are multiplexed at the device forstorage of a fused data stream; and for self-organizing systemsincluding a self-organizing data marketplace for industrial IoT data,including a self-organizing data marketplace for industrial IoT data,where available data elements are organized in the marketplace forconsumption by consumers based on training a self-organizing facilitywith a training set and feedback from measures of marketplace success,for self-organizing data pools, including self-organization of datapools based on utilization and/or yield metrics, including utilizationand/or yield metrics that are tracked for a plurality of data pools, aself-organized swarm of industrial data collectors, including aself-organizing swarm of industrial data collectors that organize amongthemselves to optimize data collection based on the capabilities andconditions of the members of the swarm, a self-organizing collector,including a self-organizing, multi-sensor data collector that canoptimize data collection, power and/or yield based on conditions in itsenvironment, a self-organizing storage for a multi-sensor datacollector, including self-organizing storage for a multi-sensor datacollector for industrial sensor data, a self-organizing network codingfor a multi-sensor data network, including self-organizing networkcoding for a data network that transports data from multiple sensors inan industrial data collection environment.

Methods and systems are disclosed herein for training artificialintelligence (“AI”) models based on industry-specific feedback,including training an AI model based on industry-specific feedback thatreflects a measure of utilization, yield, or impact, where the AI modeloperates on sensor data from an industrial environment; for anindustrial IoT distributed ledger, including a distributed ledgersupporting the tracking of transactions executed in an automated datamarketplace for industrial IoT data; for a network-sensitive collector,including a network condition-sensitive, self-organizing, multi-sensordata collector that can optimize based on bandwidth, quality of service,pricing, and/or other network conditions; for a remotely organizeduniversal data collector that can power up and down sensor interfacesbased on need and/or conditions identified in an industrial datacollection environment; and for a haptic or multi-sensory userinterface, including a wearable haptic or multi-sensory user interfacefor an industrial sensor data collector, with vibration, heat,electrical, and/or sound outputs.

Methods and systems are disclosed herein for a presentation layer foraugmented reality and virtual reality (AR/VR) industrial glasses, whereheat map elements are presented based on patterns and/or parameters incollected data; and for condition-sensitive, self-organized tuning ofAR/VR interfaces based on feedback metrics and/or training in industrialenvironments.

In embodiments, a system for data collection, processing, andutilization of signals from at least a first element in a first machinein an industrial environment includes a platform including a computingenvironment connected to a local data collection system having at leasta first sensor signal and a second sensor signal obtained from at leastthe first machine in the industrial environment. The system includes afirst sensor in the local data collection system configured to beconnected to the first machine and a second sensor in the local datacollection system. The system further includes a crosspoint switch inthe local data collection system having multiple inputs and multipleoutputs including a first input connected to the first sensor and asecond input connected to the second sensor. Throughout the presentdisclosure, wherever a crosspoint switch, multiplexer (MUX) device, orother multiple-input multiple-output data collection or communicationdevice is described, any multi-sensor acquisition device is alsocontemplated herein. In certain embodiments, a multi-sensor acquisitiondevice includes one or more channels configured for, or compatible with,an analog sensor input. The multiple outputs include a first output andsecond output configured to be switchable between a condition in whichthe first output is configured to switch between delivery of the firstsensor signal and the second sensor signal and a condition in whichthere is simultaneous delivery of the first sensor signal from the firstoutput and the second sensor signal from the second output. Each ofmultiple inputs is configured to be individually assigned to any of themultiple outputs or combined in any subsets of the inputs to theoutputs. Unassigned outputs are configured to be switched off, forexample by producing a high-impedance state.

In embodiments, the first sensor signal and the second sensor signal arecontinuous vibration data about the industrial environment. Inembodiments, the second sensor in the local data collection system isconfigured to be connected to the first machine. In embodiments, thesecond sensor in the local data collection system is configured to beconnected to a second machine in the industrial environment. Inembodiments, the computing environment of the platform is configured tocompare relative phases of the first and second sensor signals. Inembodiments, the first sensor is a single-axis sensor and the secondsensor is a three-axis sensor. In embodiments, at least one of themultiple inputs of the crosspoint switch includes internet protocol,front-end signal conditioning, for improved signal-to-noise ratio. Inembodiments, the crosspoint switch includes a third input that isconfigured with a continuously monitored alarm having a pre-determinedtrigger condition when the third input is unassigned to or undetected atany of the multiple outputs.

In embodiments, the local data collection system includes multiplemultiplexing units and multiple data acquisition units receivingmultiple data streams from multiple machines in the industrialenvironment. In embodiments, the local data collection system includesdistributed complex programmable hardware device (“CPLD”) chips eachdedicated to a data bus for logic control of the multiple multiplexingunits and the multiple data acquisition units that receive the multipledata streams from the multiple machines in the industrial environment.In embodiments, the local data collection system is configured toprovide high-amperage input capability using solid state relays. Inembodiments, the local data collection system is configured topower-down at least one of an analog sensor channel and a componentboard.

In embodiments, the local data collection system includes a phase-lockloop band-pass tracking filter configured to obtain slow-speedrevolutions per minute (“RPMs”) and phase information. In embodiments,the local data collection system is configured to digitally derive phaseusing on-board timers relative to at least one trigger channel and atleast one of the multiple inputs. In embodiments, the local datacollection system includes a peak-detector configured to autoscale usinga separate analog-to-digital converter for peak detection. Inembodiments, the local data collection system is configured to route atleast one trigger channel that is raw and buffered into at least one ofthe multiple inputs. In embodiments, the local data collection systemincludes at least one delta-sigma analog-to-digital converter that isconfigured to increase input oversampling rates to reduce sampling rateoutputs and to minimize anti-aliasing filter requirements. Inembodiments, the distributed CPLD chips each dedicated to the data busfor logic control of the multiple multiplexing units and the multipledata acquisition units includes as high-frequency crystal clockreference configured to be divided by at least one of the distributedCPLD chips for at least one delta-sigma analog-to-digital converter toachieve lower sampling rates without digital resampling.

In embodiments, the local data collection system is configured to obtainlong blocks of data at a single relatively high-sampling rate as opposedto multiple sets of data taken at different sampling rates. Inembodiments, the single relatively high-sampling rate corresponds to amaximum frequency of about forty kilohertz. In embodiments, the longblocks of data are for a duration that is in excess of one minute. Inembodiments, the local data collection system includes multiple dataacquisition units each having an onboard card set configured to storecalibration information and maintenance history of a data acquisitionunit in which the onboard card set is located. In embodiments, the localdata collection system is configured to plan data acquisition routesbased on hierarchical templates.

In embodiments, the local data collection system is configured to managedata collection bands. In embodiments, the data collection bands definea specific frequency band and at least one of a group of spectral peaks,a true-peak level, a crest factor derived from a time waveform, and anoverall waveform derived from a vibration envelope. In embodiments, thelocal data collection system includes a neural net expert system usingintelligent management of the data collection bands. In embodiments, thelocal data collection system is configured to create data acquisitionroutes based on hierarchical templates that each include the datacollection bands related to machines associated with the dataacquisition routes. In embodiments, at least one of the hierarchicaltemplates is associated with multiple interconnected elements of thefirst machine. In embodiments, at least one of the hierarchicaltemplates is associated with similar elements associated with at leastthe first machine and a second machine. In embodiments, at least one ofthe hierarchical templates is associated with at least the first machinebeing proximate in location to a second machine.

In embodiments, the local data collection system includes a graphicaluser interface (“GUI”) system configured to manage the data collectionbands. In embodiments, the GUI system includes an expert systemdiagnostic tool. In embodiments, the platform includes cloud-based,machine pattern analysis of state information from multiple sensors toprovide anticipated state information for the industrial environment. Inembodiments, the platform is configured to provide self-organization ofdata pools based on at least one of the utilization metrics and yieldmetrics. In embodiments, the platform includes a self-organized swarm ofindustrial data collectors. In embodiments, the local data collectionsystem includes a wearable haptic user interface for an industrialsensor data collector with at least one of vibration, heat, electrical,and sound outputs.

In embodiments, multiple inputs of the crosspoint switch include a thirdinput connected to the second sensor and a fourth input connected to thesecond sensor. The first sensor signal is from a single-axis sensor atan unchanging location associated with the first machine. Inembodiments, the second sensor is a three-axis sensor. In embodiments,the local data collection system is configured to record gap-freedigital waveform data simultaneously from at least the first input, thesecond input, the third input, and the fourth input. In embodiments, theplatform is configured to determine a change in relative phase based onthe simultaneously recorded gap-free digital waveform data. Inembodiments, the second sensor is configured to be movable to aplurality of positions associated with the first machine while obtainingthe simultaneously recorded gap-free digital waveform data. Inembodiments, multiple outputs of the crosspoint switch include a thirdoutput and fourth output. The second, third, and fourth outputs areassigned together to a sequence of tri-axial sensors each located atdifferent positions associated with the machine. In embodiments, theplatform is configured to determine an operating deflection shape basedon the change in relative phase and the simultaneously recorded gap-freedigital waveform data.

In embodiments, the unchanging location is a position associated withthe rotating shaft of the first machine. In embodiments, tri-axialsensors in the sequence of the tri-axial sensors are each located atdifferent positions on the first machine but are each associated withdifferent bearings in the machine. In embodiments, tri-axial sensors inthe sequence of the tri-axial sensors are each located at similarpositions associated with similar bearings but are each associated withdifferent machines. In embodiments, the local data collection system isconfigured to obtain the simultaneously recorded gap-free digitalwaveform data from the first machine while the first machine and asecond machine are both in operation. In embodiments, the local datacollection system is configured to characterize a contribution from thefirst machine and the second machine in the simultaneously recordedgap-free digital waveform data from the first machine. In embodiments,the simultaneously recorded gap-free digital waveform data has aduration that is in excess of one minute.

In embodiments, a method of monitoring a machine having at least oneshaft supported by a set of bearings includes monitoring a first datachannel assigned to a single-axis sensor at an unchanging locationassociated with the machine. The method includes monitoring second,third, and fourth data channels each assigned to an axis of a three-axissensor. The method includes recording gap-free digital waveform datasimultaneously from all of the data channels while the machine is inoperation and determining a change in relative phase based on thedigital waveform data.

In embodiments, the tri-axial sensor is located at a plurality ofpositions associated with the machine while obtaining the digitalwaveform. In embodiments, the second, third, and fourth channels areassigned together to a sequence of tri-axial sensors each located atdifferent positions associated with the machine. In embodiments, thedata is received from all of the sensors simultaneously. In embodiments,the method includes determining an operating deflection shape based onthe change in relative phase information and the waveform data. Inembodiments, the unchanging location is a position associated with theshaft of the machine. In embodiments, the tri-axial sensors in thesequence of the tri-axial sensors are each located at differentpositions and are each associated with different bearings in themachine. In embodiments, the unchanging location is a positionassociated with the shaft of the machine. The tri-axial sensors in thesequence of the tri-axial sensors are each located at differentpositions and are each associated with different bearings that supportthe shaft in the machine.

In embodiments, the method includes monitoring the first data channelassigned to the single-axis sensor at an unchanging location located ona second machine. The method includes monitoring the second, the third,and the fourth data channels, each assigned to the axis of a three-axissensor that is located at the position associated with the secondmachine. The method also includes recording gap-free digital waveformdata simultaneously from all of the data channels from the secondmachine while both of the machines are in operation. In embodiments, themethod includes characterizing the contribution from each of themachines in the gap-free digital waveform data simultaneously from thesecond machine.

In embodiments, a method for data collection, processing, andutilization of signals with a platform monitoring at least a firstelement in a first machine in an industrial environment includesobtaining, automatically with a computing environment, at least a firstsensor signal and a second sensor signal with a local data collectionsystem that monitors at least the first machine. The method includesconnecting a first input of a crosspoint switch of the local datacollection system to a first sensor and a second input of the crosspointswitch to a second sensor in the local data collection system. Themethod includes switching between a condition in which a first output ofthe crosspoint switch alternates between delivery of at least the firstsensor signal and the second sensor signal and a condition in whichthere is simultaneous delivery of the first sensor signal from the firstoutput and the second sensor signal from a second output of thecrosspoint switch. The method also includes switching off unassignedoutputs of the crosspoint switch into a high-impedance state.

In embodiments, the first sensor signal and the second sensor signal arecontinuous vibration data from the industrial environment. Inembodiments, the second sensor in the local data collection system isconnected to the first machine. In embodiments, the second sensor in thelocal data collection system is connected to a second machine in theindustrial environment. In embodiments, the method includes comparing,automatically with the computing environment, relative phases of thefirst and second sensor signals. In embodiments, the first sensor is asingle-axis sensor and the second sensor is a three-axis sensor. Inembodiments, at least the first input of the crosspoint switch includesinternet protocol front-end signal conditioning for improvedsignal-to-noise ratio.

In embodiments, the method includes continuously monitoring at least athird input of the crosspoint switch with an alarm having apre-determined trigger condition when the third input is unassigned toany of multiple outputs on the crosspoint switch. In embodiments, thelocal data collection system includes multiple multiplexing units andmultiple data acquisition units receiving multiple data streams frommultiple machines in the industrial environment. In embodiments, thelocal data collection system includes distributed CPLD chips eachdedicated to a data bus for logic control of the multiple multiplexingunits and the multiple data acquisition units that receive the multipledata streams from the multiple machines in the industrial environment.In embodiments, the local data collection system provides high-amperageinput capability using solid state relays.

In embodiments, the method includes powering down at least one of ananalog sensor channel and a component board of the local data collectionsystem. In embodiments, the local data collection system includes anexternal voltage reference for an A/D zero reference that is independentof the voltage of the first sensor and the second sensor. Inembodiments, the local data collection system includes a phase-lock loopband-pass tracking filter that obtains slow-speed RPMs and phaseinformation. In embodiments, the method includes digitally derivingphase using on-board timers relative to at least one trigger channel andat least one of multiple inputs on the crosspoint switch.

In embodiments, the method includes auto-scaling with a peak-detectorusing a separate analog-to-digital converter for peak detection. Inembodiments, the method includes routing at least one trigger channelthat is raw and buffered into at least one of multiple inputs on thecrosspoint switch. In embodiments, the method includes increasing inputoversampling rates with at least one delta-sigma analog-to-digitalconverter to reduce sampling rate outputs and to minimize anti-aliasingfilter requirements. In embodiments, the distributed CPLD chips are eachdedicated to the data bus for logic control of the multiple multiplexingunits and the multiple data acquisition units and each include ahigh-frequency crystal clock reference divided by at least one of thedistributed CPLD chips for at least one delta-sigma analog-to-digitalconverter to achieve lower sampling rates without digital resampling. Inembodiments, the method includes obtaining long blocks of data at asingle relatively high-sampling rate with the local data collectionsystem as opposed to multiple sets of data taken at different samplingrates. In embodiments, the single relatively high-sampling ratecorresponds to a maximum frequency of about forty kilohertz. Inembodiments, the long blocks of data are for a duration that is inexcess of one minute. In embodiments, the local data collection systemincludes multiple data acquisition units and each data acquisition unithas an onboard card set that stores calibration information andmaintenance history of a data acquisition unit in which the onboard cardset is located.

In embodiments, the method includes planning data acquisition routesbased on hierarchical templates associated with at least the firstelement in the first machine in the industrial environment. Inembodiments, the local data collection system manages data collectionbands that define a specific frequency band and at least one of a groupof spectral peaks, a true-peak level, a crest factor derived from a timewaveform, and an overall waveform derived from a vibration envelope. Inembodiments, the local data collection system includes a neural netexpert system using intelligent management of the data collection bands.In embodiments, the local data collection system creates dataacquisition routes based on hierarchical templates that each include thedata collection bands related to machines associated with the dataacquisition routes. In embodiments, at least one of the hierarchicaltemplates is associated with multiple interconnected elements of thefirst machine. In embodiments, at least one of the hierarchicaltemplates is associated with similar elements associated with at leastthe first machine and a second machine. In embodiments, at least one ofthe hierarchical templates is associated with at least the first machinebeing proximate in location to a second machine.

In embodiments, the method includes controlling a GUI system of thelocal data collection system to manage the data collection bands. TheGUI system includes an expert system diagnostic tool. In embodiments,the computing environment of the platform includes cloud-based, machinepattern analysis of state information from multiple sensors to provideanticipated state information for the industrial environment. Inembodiments, the computing environment of the platform providesself-organization of data pools based on at least one of the utilizationmetrics and yield metrics. In embodiments, the computing environment ofthe platform includes a self-organized swarm of industrial datacollectors. In embodiments, each of multiple inputs of the crosspointswitch is individually assignable to any of multiple outputs of thecrosspoint switch.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for capturing a plurality ofstreams of sensed data from sensors deployed to monitor aspects of anindustrial machine associated with at least one moving part of themachine; at least one of the streams contains a plurality of frequenciesof data. The method may include identifying a subset of data in at leastone of the plurality of streams that corresponds to data representing atleast one predefined frequency. The at least one predefined frequency isrepresented by a set of data collected from alternate sensors deployedto monitor aspects of the industrial machine associated with the atleast one moving part of the machine. The method may further includeprocessing the identified data with a data processing facility thatprocesses the identified data with an algorithm configured to be appliedto the set of data collected from alternate sensors. Lastly, the methodmay include storing the at least one of the streams of data, theidentified subset of data, and a result of processing the identifieddata in an electronic data set.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing, andstorage systems and may include a method for applying data captured fromsensors deployed to monitor aspects of an industrial machine associatedwith at least one moving part of the machine. The data is captured withpredefined lines of resolution covering a predefined frequency range andis sent to a frequency matching facility that identifies a subset ofdata streamed from other sensors deployed to monitor aspects of theindustrial machine associated with at least one moving part of themachine. The streamed data includes a plurality of lines of resolutionand frequency ranges. The subset of data identified corresponds to thelines of resolution and predefined frequency range. This method mayinclude storing the subset of data in an electronic data record in aformat that corresponds to a format of the data captured with predefinedlines of resolution and signaling to a data processing facility thepresence of the stored subset of data. This method may, optionally,include processing the subset of data with at least one set ofalgorithms, models and pattern recognizers that corresponds toalgorithms, models and pattern recognizers associated with processingthe data captured with predefined lines of resolution covering apredefined frequency range.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for identifying a subset ofstreamed sensor data, the sensor data captured from sensors deployed tomonitor aspects of an industrial machine associated with at least onemoving part of the machine, the subset of streamed sensor data atpredefined lines of resolution for a predefined frequency range, andestablishing a first logical route for communicating electronicallybetween a first computing facility performing the identifying and asecond computing facility. In embodiments, identified subset of thestreamed sensor data is communicated exclusively over the establishedfirst logical route when communicating the subset of streamed sensordata from the first facility to the second facility. This method mayfurther include establishing a second logical route for communicatingelectronically between the first computing facility and the secondcomputing facility for at least one portion of the streamed sensor datathat is not the identified subset. Additionally, this method may furtherinclude establishing a third logical route for communicatingelectronically between the first computing facility and the secondcomputing facility for at least one portion of the streamed sensor datathat includes the identified subset and at least one other portion ofthe data not represented by the identified subset.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a first data sensing and processingsystem that captures first data from a first set of sensors deployed tomonitor aspects of an industrial machine associated with at least onemoving part of the machine, the first data covering a set of lines ofresolution and a frequency range. This system may include a second datasensing and processing system that captures and streams a second set ofdata from a second set of sensors deployed to monitor aspects of theindustrial machine associated with at least one moving part of themachine, the second data covering a plurality of lines of resolutionthat includes the set of lines of resolution and a plurality offrequencies that includes the frequency range. The system may enableselecting a portion of the second data that corresponds to the set oflines of resolution and the frequency range of the first data andprocessing the selected portion of the second data with the first datasensing and processing system.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for automatically processing aportion of a stream of sensed data. The sensed data is received from afirst set of sensors deployed to monitor aspects of an industrialmachine associated with at least one moving part of the machine. Thesensed data is in response to an electronic data structure thatfacilitates extracting a subset of the stream of sensed data thatcorresponds to a set of sensed data received from a second set ofsensors deployed to monitor the aspects of the industrial machineassociated with the at least one moving part of the machine. The set ofsensed data is constrained to a frequency range. The stream of senseddata includes a range of frequencies that exceeds the frequency range ofthe set of sensed data, the processing comprising executing an algorithmon a portion of the stream of sensed data that is constrained to thefrequency range of the set of sensed data, the algorithm configured toprocess the set of sensed data.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for receiving first data fromsensors deployed to monitor aspects of an industrial machine associatedwith at least one moving part of the machine. This method may furtherinclude detecting at least one of a frequency range and lines ofresolution represented by the first data; receiving a stream of datafrom sensors deployed to monitor the aspects of the industrial machineassociated with the at least one moving part of the machine. The streamof data includes: (1) a plurality of frequency ranges and a plurality oflines of resolution that exceeds the frequency range and the lines ofresolution represented by the first data; (2) a set of data extractedfrom the stream of data that corresponds to at least one of thefrequency range and the lines of resolution represented by the firstdata; and (3) the extracted set of data which is processed with a dataprocessing algorithm that is configured to process data within thefrequency range and within the lines of resolution of the first data.

Methods and systems are provided herein for using mobile devices,including wearable devices, mobile robots, mobile vehicles, and/orhandheld devices, to identify states of targets within an industrialenvironment. The mobile devices include one or more sensors that may beconfigured to record state-related measurements of the target, forexample, based on vibrational, temperature, electrical, magnetic, sound,and/or other measurements. The data captured using some or all of thesemobile devices may be processed by intelligent systems onboard thosemobile devices and/or at a server in communication with those mobiledevices over a network. The intelligent systems include intelligence forprocessing the data captured using the respective mobile devices.Processing the data can, for example, include identifying a state of atarget for which measurements were recorded by comparing thestate-related measurements from the wearable device against informationstored in a database, which may, for example, be part of a knowledgebase associated with the industrial environment. In embodiments,corrective actions may be identified and taken in response to thestate-related measurements captured using the mobile devices.

In embodiments, a method for using a wearable device to identify a stateof a target of an industrial environment is disclosed. In embodiments,the method comprises recording a state-related measurement of the targetusing one or more sensors of the wearable device; transmitting thestate-related measurement to a server over a network; using intelligentsystems associated with the server to process the state-relatedmeasurement against pre-recorded data for the target. In embodiments,processing the state-related measurement against the pre-recorded datafor the target includes identifying the pre-recorded data for the targetwithin a knowledge base associated with the industrial environment; andidentifying, as the state of the target, a state indicated by thepre-recorded data for the target within the knowledge base.

In embodiments, a system for identifying a state of a target of anindustrial environment is disclosed. In embodiments, the systemcomprises a first wearable device including one or more sensorsconfigured to record a first type of state-related measurement; a secondwearable device including one or more sensors configured to record asecond type of state-related measurement; and a server that receives thefirst type of state-related measurement from the first wearable deviceand the second type of state-related measurement from the secondwearable device, the server including intelligent systems configured to:process the first type of state-related measurement and the second typeof state-related measurement against pre-recorded data stored within aknowledge base to identify the state of the target; and update thepre-recorded data according to at least one of the first type ofstate-related measurement or the second type of state-relatedmeasurement.

In embodiments, a method for using a mobile data collector to identify astate of a target of an industrial environment is disclosed. Inembodiments, the method comprises controlling the mobile data collectorto approach a location of the target within the industrial environment;recording a state-related measurement of the target using one or moresensors of the mobile data collector; transmitting the state-relatedmeasurement to a server over a network; using intelligent systemsassociated with the server to process the state-related measurementagainst pre-recorded data for the target. In embodiments, processing thestate-related measurement against the pre-recorded data for the targetincludes identifying the pre-recorded data for the target within aknowledge base associated with the industrial environment; andidentifying, as the state of the target, a state indicated by thepre-recorded data for the target within the knowledge base.

In embodiments, a system for identifying a state of a target of anindustrial environment is disclosed. In embodiments, the systemcomprises a first mobile data collector including one or more sensorsconfigured to record a first type of state-related measurement; a secondmobile data collector including one or more sensors configured to recorda second type of state-related measurement; and a server that receivesthe first type of state-related measurement from the first mobile datacollector and the second type of state-related measurement from thesecond mobile data collector, the server including intelligent systemsconfigured to: process the first type of state-related measurement andthe second type of state-related measurement against pre-recorded datastored within a knowledge base to identify the state of the target; andupdate the pre-recorded data according to at least one of the first typeof state-related measurement or the second type of state-relatedmeasurement.

In embodiments, a method for using a handheld device to identify a stateof a target of an industrial environment is disclosed. In embodiments,the method comprises recording a state-related measurement of the targetusing one or more sensors of the handheld device; transmitting thestate-related measurement to a server over a network; using intelligentsystems associated with the server to process the state-relatedmeasurement against pre-recorded data for the target. In embodiments,processing the state-related measurement against the pre-recorded datafor the target includes identifying the pre-recorded data for the targetwithin a knowledge base associated with the industrial environment; andidentifying, as the state of the target, a state indicated by thepre-recorded data for the target within the knowledge base.

In embodiments, a system for identifying a state of a target of anindustrial environment is disclosed. In embodiments, the systemcomprises a first handheld device including one or more sensorsconfigured to record a first type of state-related measurement; a secondhandheld device including one or more sensors configured to record asecond type of state-related measurement; and a server that receives thefirst type of state-related measurement from the first handheld deviceand the second type of state-related measurement from the secondhandheld device, the server including intelligent systems configured to:process the first type of state-related measurement and the second typeof state-related measurement against pre-recorded data stored within aknowledge base to identify the state of the target; and update thepre-recorded data according to at least one of the first type ofstate-related measurement or the second type of state-relatedmeasurement.

Methods and systems are provided herein for a computer vision systemconfigured to identify operating characteristics, such as vibration orother suitable characteristics, of one or more industrial IoT devicesusing input from one or more data capture devices. The one or more datacapture devices may include image data capture devices that capturevisible and non-visible light, sensors that measure variouscharacteristics of the one or more industrial IoT devices, or othersuitable data capture devices. The computer vision system is configuredto generate image data sets from the input and to analyze the visualaspects of the image data sets in order to identify operatingcharacteristics of the industrial IoT devices. Further, the computervision system is configured to determine whether to take correctiveaction in response to the operating characteristics of the industrialIoT devices.

In embodiments, an apparatus for detecting operating characteristics ofa manufacturing device includes a memory and a processor. The memoryincludes instructions executable by the processor to generate one ormore image data sets using raw data captured by one or more data capturedevices. The memory further includes instructions executable by theprocessor to identify one or more values corresponding to a portion ofthe manufacturing device within a point of interest represented by theone or more image data sets. The memory further includes instructionsexecutable by the processor to record the one or more values; comparethe recorded one or more values to corresponding predicted values and togenerate a variance data set based on the comparison of the recorded oneor more values and the corresponding predicted values. The memoryfurther includes instructions executable by the processor to identify anoperating characteristic of the manufacturing device based on thevariance data and to generate an indication indicating the operatingcharacteristic.

In embodiments, a method for detecting operating characteristics of amanufacturing device includes generating one or more image data setsusing raw data captured by one or more data capture devices. The methodalso includes identifying one or more values corresponding to a portionof the manufacturing device within a point of interest represented bythe one or more image data sets; recording the one or more values andcomparing the recorded one or more values to corresponding predictedvalues. The method also includes generating a variance data set based onthe comparison of the recorded on or more values and the correspondingpredicted values and identifying an operating characteristic of themanufacturing device based on the variance data. The method alsoincludes generating an indication indicating the operatingcharacteristic.

In embodiments, a system for detecting operating characteristics of amanufacturing device includes at least one data capture deviceconfigured to capture raw data of a point of interest of themanufacturing device, a memory, and a processor. The memory includesinstructions executable by the processor to generate one or more imagedata sets using the raw data captured and to identify one or more valuescorresponding to a portion of the manufacturing device within the pointof interest represented by the one or more image data sets. The memoryfurther includes instructions executable by the processor to record theone or more values and to compare the recorded one or more values tocorresponding predicted values. The memory further includes instructionsexecutable by the processor to generate a variance data set based on thecomparison of the recorded on or more values and the correspondingpredicted values, to identify an operating characteristic of themanufacturing device based on the variance data, and to generate anindication indicating the operating characteristic.

In embodiments, a computer vision system for detecting operatingcharacteristics of a manufacturing device, includes at least one datacapture device configured to capture raw data of a point of interest ofthe manufacturing device, a memory, and a processor. The memory includesinstructions executable by the processor to generate one or more imagedata sets using the raw data captured and to visually identify one ormore values corresponding to a portion of the manufacturing devicewithin the point of interest represented by the one or more image datasets. The memory further includes instructions executable by theprocessor to record the one or more values and to visually compare therecorded one or more values to corresponding predicted values. Thememory further includes instructions executable by the processor togenerate a variance data set based on the comparison of the recorded onor more values and the corresponding predicted values and to identify anoperating characteristic of the manufacturing device based on thevariance data. The memory further includes instructions executable bythe processor to compare the operating characteristic to a threshold andto determine whether the operating characteristic is within a tolerancebased on whether the operating characteristic is greater than thethreshold. The memory further includes instructions executable by theprocessor to generate an indication indicating the operatingcharacteristic.

In embodiments, a computer vision system for detecting operatingcharacteristics of a device, includes at least one data capture deviceconfigured to capture raw data of a point of interest of the device, amemory and a processor. The memory includes instructions executable bythe processor to generate one or more image data sets using the raw datacaptured and visually identify one or more values corresponding to aportion of the device within the point of interest represented by theone or more image data sets. The memory further includes instructionsexecutable by the processor to record the one or more values and tovisually compare the recorded one or more values to correspondingpredicted values. The memory further includes instructions executable bythe processor to generate a variance data set based on the comparison ofthe recorded on or more values and the corresponding predicted values.The memory includes instructions executable by the processor to identifyan operating characteristic of the device based on the variance data andto compare the operating characteristic to a threshold. The memoryincludes instructions executable by the processor to determine whetherthe operating characteristic is within a tolerance based on whether theoperating characteristic is greater than the threshold and to generatean indication indicating the operating characteristic.

Methods and systems are provided herein as including combinations ofembodiments disclosed herein. In embodiments, a method comprises:receiving vibration data representative of a vibration of at least aportion of an industrial machine from a wearable device including atleast one vibration sensor used to capture the vibration data;determining a frequency of the captured vibration by processing thecaptured vibration data; determining, based on the frequency, a segmentof a multi-segment vibration frequency spectra that bounds the capturedvibration; calculating a severity unit for the captured vibration basedon the determined segment; and generating a signal in a predictivemaintenance circuit for executing a maintenance action on at least theportion of the industrial machine based on the severity unit. Inembodiments, the at least one vibration sensor of the wearable devicecaptures the vibration data based on a waveform derived from a vibrationenvelope associated with at least the portion of the industrial machine.In embodiments, the method further comprises: detecting, using thewearable device, that the industrial machine is in near proximity to thewearable device; and causing the wearable device to capture thevibration data responsive to detecting the near proximity of theindustrial machine to the wearable device. In embodiments, the methodfurther comprises: detecting a vibration level change of at least theportion of the industrial machine using the at least one vibrationsensor of the wearable device; and using the wearable device to capturethe vibration data responsive to detecting the vibration level change.In embodiments, the method further comprises transmitting the signal tothe wearable device to cause the execution of the maintenance action. Inembodiments, calculating the severity unit for the captured vibrationbased on the determined segment comprises: mapping the capturedvibration to the severity unit based on the determined segment by:mapping the captured vibration to a first severity unit when thefrequency of the captured vibration corresponds to a below a low-endknee threshold-range of the multi-segment vibration frequency spectra;mapping the captured vibration to a second severity unit when thefrequency of the captured vibration corresponds to a mid-range of themulti-segment vibration frequency spectra; and mapping the capturedvibration to a third severity unit when the frequency of the capturedvibration corresponds to an above the high-end knee threshold-range ofthe multi-segment vibration frequency spectra. In embodiments, themethod further comprises training an intelligent system to determinewhether a vibration maps to the first severity unit, the second severityunit, or the third severity unit. In embodiments, the severity unitrepresents an impact on at least the portion of the industrial machineof the maintenance action based on the captured vibration data. Inembodiments, the method further comprises determining an amplitude and agravitational force of the captured vibration data by the processing ofthe captured vibration data. In embodiments, calculating the severityunit for the captured vibration comprises calculating the severity unitbased on the determined segment and at least one of the amplitude or thegravitational force. In embodiments, the severity unit represents thecaptured vibration independent of the frequency. In embodiments, atleast one of the signals or the maintenance action indicates, based onthe severity unit, increasing or decreasing a frequency for collectionand analysis of further vibration data using the at least one vibrationsensor. In embodiments, the maintenance action indicates to perform oneof calibration, diagnostic testing, or visual inspection against atleast the portion of the industrial machine. In embodiments, the methodfurther comprises transmitting the signal to a component of theindustrial machine. In embodiments, the maintenance action indicates toresurvey at least the portion of the industrial machine. In embodiments,the component of the industrial machine causes the execution of themaintenance action responsive to receiving the signal. In embodiments,the wearable device is a first wearable device of a plurality ofwearable devices integrated within an industrial platform. Inembodiments, a second wearable device of the plurality of wearabledevices captures a temperature of the industrial machine using atemperature sensor. In embodiments, the signal is generated based on theseverity unit and based on a second severity unit calculated based onthe captured temperature. In embodiments, a third wearable device of theplurality of wearable devices captures an electrical output orelectrical use of the industrial machine using an electricity sensor. Inembodiments, the signal is generated based on the severity unit andbased on a third severity unit calculated based on the capturedelectrical output or electrical use. In embodiments, a fourth wearabledevice of the plurality of wearable devices captures a level or changein an electromagnetic field of the industrial machine using a magneticsensor. In embodiments, the signal is generated based on the severityunit and based on a fourth severity unit calculated based on thecaptured level or change in the electromagnetic field. In embodiments, afifth wearable device of the plurality of wearable devices captures asound wave output from the industrial machine using a sound sensor. Inembodiments, the signal is generated based on the severity unit andbased on a fifth severity unit calculated based on the captured soundwave. In embodiments, the wearable device is a first wearable deviceintegrated within an article of clothing. In embodiments, the methodfurther comprises using a second wearable device integrated within anaccessory article.

In embodiments, a method comprises: deploying a mobile data collectorfor detecting and monitoring vibration activity of at least a portion ofan industrial machine, the mobile data collector including one or morevibration sensors; determining a severity of the vibration activityrelative to timing by processing vibration data representative of thevibration activity and generated using the one or more vibrationsensors; and predicting one or more maintenance actions to perform withrespect to at least the portion of the industrial machine based on theseverity of the vibration activity. In embodiments, determining theseverity of the vibration data relative to the timing by processing thevibration data representative of the vibration activity and generatedusing the one or more vibration sensors comprises: determining afrequency of the vibration activity by processing the vibration data;determining, based on the frequency, a segment of a multi-segmentvibration frequency spectra that bounds the vibration activity; andcalculating a severity unit for the vibration activity based on thedetermined segment of the multi-segment vibration frequency spectra. Inembodiments, calculating the severity unit for the vibration activitybased on the determined segment of the multi-segment vibration frequencyspectra comprises: mapping the vibration activity to the severity unitbased on the determined segment of the multi-segment vibration frequencyspectra by: mapping the vibration activity to a first severity unit whenthe frequency of the vibration activity corresponds to a below a low-endknee threshold-range of the multi-segment vibration frequency spectra;mapping the vibration activity to a second severity unit when thefrequency of the vibration activity corresponds to a mid-range of themulti-segment vibration frequency spectra; and mapping the vibrationactivity to a third severity unit when the frequency of the vibrationactivity corresponds to an above the high-end knee threshold-range ofthe multi-segment vibration frequency spectra. In embodiments, themethod further comprises causing the at least one of the mobile datacollectors to perform the maintenance action. In embodiments, the methodfurther comprises: controlling the mobile data collector to approach alocation of the industrial machine within an industrial environment thatincludes the industrial machine; causing the one or more vibrationsensors of the mobile data collector to record one or more measurementsof the vibration activity; and transmitting the one or more measurementsof the vibration activity as the vibration data to a server over anetwork. In embodiments, the vibration data is processed at the serverto determine the severity of the vibration activity. In embodiments,predicting the one or more maintenance actions to perform with respectto at least the portion of the industrial machine based on the severityof the vibration activity comprises: using intelligent systemsassociated with the server to process the vibration data againstpre-recorded data for the industrial machine. In embodiments, processingthe vibration data against the pre-recorded data for the industrialmachine includes identifying the pre-recorded data for the industrialmachine within a knowledge base associated with the industrialenvironment; and identifying an operating characteristic of at least theportion of the machine based on the pre-recorded data for the industrialmachine within the knowledge base; and predicting the one or moremaintenance actions based on the operating characteristic. Inembodiments, the vibration activity is indicative of a waveform derivedfrom a vibration envelope associated with the industrial machine. Inembodiments, the one or more vibration sensors detect the vibrationactivity when the mobile data collector is in near proximity to theindustrial machine. In embodiments, the vibration activity representsvelocity information for at least the portion of the industrial machine.In embodiments, the vibration activity represents frequency informationfor at least the portion of the industrial machine. In embodiments, themobile data collector is a mobile robot. In embodiments, the mobile datacollector is a mobile vehicle. In embodiments, the mobile data collectoris one of a plurality of mobile data collectors of a mobile datacollector swarm. In embodiments, the method further comprises usingself-organization systems of the mobile data collector swarm to controlmovements of the mobile data collector within an industrial environmentthat includes the industrial machine. In embodiments, the one or morevibration sensors detect the vibration activity when the mobile datacollector is in near proximity to the industrial machine. Inembodiments, using the self-organization systems of the mobile datacollector swarm to control the movements of the mobile data collectorwithin the industrial environment comprises controlling the movements ofthe mobile data collector within the industrial environment based onmovements of at least one other mobile data collector of the pluralityof mobile data collectors. In embodiments, the mobile data collector isa mobile robot and at least one other mobile data collector of theplurality of mobile data collectors is a mobile vehicle.

In embodiments, an industrial machine predictive maintenance systemcomprises: a mobile data collector swarm comprising one or more mobiledata collectors configured to collect health monitoring datarepresentative of conditions of one or more industrial machines locatedin an industrial environment; an industrial machine predictivemaintenance facility that produces industrial machine servicerecommendations responsive to the health monitoring data by applyingmachine fault detection and classification algorithms thereto; and acomputerized maintenance management system (CMMS) that produces at leastone of the orders and requests for service and parts responsive toreceiving the industrial machine service recommendations. Inembodiments, the industrial machine predictive maintenance systemfurther comprises a service and delivery coordination facility thatreceives and processes information regarding services performed onindustrial machines responsive to the at least one of orders andrequests for service and parts, thereby validating the servicesperformed while producing a ledger of service activity and results forindividual industrial machines. In embodiments, the ledger uses ablockchain structure to track records of transactions for each of the atleast one of the orders and the requests for service and parts. Inembodiments, each record is stored as a block in the blockchainstructure. In embodiments, the CMMS generates subsequent blocks of theledger by combining data from at least one of shipment readiness,installation, operational sensor data, service events, parts orders,service orders, or diagnostic activity with a hash of a most recentlygenerated block in the ledger. In embodiments, the industrial machinepredictive maintenance system further comprises a self-organizationsystem that controls movements of the one or more mobile data collectorswithin the industrial environment. In embodiments, the self-organizationsystem transmits requests for the health monitoring data to the one ormore mobile data collectors. In embodiments, the mobile data collectorstransmit the health monitoring data to the self-organization systemresponsive to the requests. In embodiments, the self-organizationtransmits the health monitoring data to the industrial machinepredictive maintenance facility. In embodiments, the industrial machinepredictive maintenance system further comprises a data collection routerthat receives the health monitoring data from the one or more mobiledata collectors when the mobile data collectors are in near proximity tothe data collection router. In embodiments, the data collection routertransmits the health monitoring data to the industrial machinepredictive maintenance facility. In embodiments, the one or more mobiledata collectors push the health monitoring data to the data collectionrouter. In embodiments, the data collection router pulls the healthmonitoring data from the one or more mobile data collectors. Inembodiments, the industrial machine predictive maintenance systemfurther comprises a self-organization system that controls movements ofthe one or more mobile data collectors within the industrialenvironment. In embodiments, the self-organization system controlscommunications of the health monitoring data from the one or more mobiledata collectors to the data collection router. In embodiments, eachmobile data collector of the one or more mobile data collectors is oneof a mobile robot including one or more integrated sensors, a mobilerobot including one or more coupled sensors, a mobile vehicle with oneor more integrated sensors, or a mobile vehicle with one or more coupledsensors. In embodiments, the industrial machine predictive maintenancefacility produces the industrial machine service recommendations basedon severity units calculated for the health monitoring data.

In embodiments, a system comprises: a plurality of wearable devicesintegrated within an industrial uniform, each wearable device of theindustrial uniform comprising one or more sensors that collectmeasurements from industrial machines located in an industrialenvironment, the measurements representative of conditions of theindustrial machines; an industrial machine predictive maintenancefacility that produces industrial machine service recommendations basedon the measurements by applying machine fault detection andclassification algorithms thereto; and a computerized maintenancemanagement system (CMMS) that produces at least one of orders andrequests for service and parts responsive to receiving the industrialmachine service recommendations. In embodiments, the system furthercomprises a service and delivery coordination facility that receives andprocesses information regarding services performed on industrialmachines responsive to the at least one of orders and requests forservice and parts, thereby validating the services performed whileproducing a ledger of service activity and results for individualindustrial machines. In embodiments, the ledger uses a blockchainstructure to track records of transactions for each of the at least oneof the orders and the requests for service and parts. In embodiments,each record is stored as a block in the blockchain structure. Inembodiments, the CMMS generates subsequent blocks of the ledger bycombining data from at least one of shipment readiness, installation,operational sensor data, service events, parts orders, service orders,or diagnostic activity with a hash of a most recently generated block inthe ledger. In embodiments, the one or more sensors of a first wearabledevice of the industrial uniform includes a sensor configured to collectvibration measurements from at least one of the industrial machines. Inembodiments, the one or more sensors of a second wearable device of theindustrial uniform includes a sensor configured to collect temperaturemeasurements from at least one of the industrial machines. Inembodiments, the one or more sensors of a first wearable device of theindustrial uniform includes a sensor configured to collect electricalmeasurements from at least one of the industrial machines. Inembodiments, the one or more sensors of a first wearable device of theindustrial uniform includes a sensor configured to collect magneticmeasurements from at least one of the industrial machines. Inembodiments, the one or more sensors of a first wearable device of theindustrial uniform includes a sensor configured to collect soundmeasurements from at least one of the industrial machines. Inembodiments, a first wearable device of the industrial uniform is anarticle of clothing and a second wearable device of the industrialuniform is an accessory article. In embodiments, the system furthercomprises a collective processing mind that controls the collection ofmeasurements of the one or more industrial machines by the plurality ofwearable devices. In embodiments, the collective processing mindtransmits a first command to a wearable device of the industrial uniformto cause the one or more sensors of the wearable device to collect themeasurements of the one or more industrial machines. In embodiments, thecollective processing mind transmits a second command to the wearabledevice to cause the wearable device to transmit the measurements to thecollective processing mind. In embodiments, the industrial machinepredictive maintenance facility produces the industrial machine servicerecommendations based on severity units calculated for the measurements.

In embodiments, a system comprises: a plurality of wearable devicesintegrated within an industrial uniform, each wearable device of theindustrial uniform comprising one or more sensors that collectmeasurements from industrial machines located in an industrialenvironment, the measurements representative of conditions of theindustrial machines; an industrial machine predictive maintenancefacility that produces industrial machine service recommendations basedon the measurements by applying machine fault detection andclassification algorithms thereto; a computerized maintenance managementsystem (CMMS) that produces at least one of orders and requests forservice and parts responsive to receiving the industrial machine servicerecommendations; and a service and delivery coordination facility thatreceives and processes information regarding services performed onindustrial machines responsive to the at least one of orders andrequests for service and parts, thereby validating the servicesperformed while producing a ledger of service activity and results forindividual industrial machines. In embodiments, the industrial machinepredictive maintenance facility produces the industrial machine servicerecommendations based on severity units calculated for the measurements.In embodiments, the ledger uses a blockchain structure to track recordsof transactions for each of the at least one of the orders and therequests for service and parts. In embodiments, each record is stored asa block in the blockchain structure.

In embodiments, a system comprises: a mobile data collector swarmcomprising one or more mobile data collectors configured to collecthealth monitoring data representative of conditions of one or moreindustrial machines located in an industrial environment; an industrialmachine predictive maintenance facility that produces industrial machineservice recommendations responsive to the health monitoring data byapplying machine fault detection and classification algorithms thereto;a computerized maintenance management system (CMMS) that produces atleast one of orders and requests for service and parts responsive toreceiving the industrial machine service recommendations; and a serviceand delivery coordination facility that receives and processesinformation regarding services performed on industrial machinesresponsive to the at least one of orders and requests for service andparts, thereby validating the services performed while producing aledger of service activity and results for individual industrialmachines. In embodiments, the industrial machine predictive maintenancefacility produces the industrial machine service recommendations basedon severity units calculated for the health monitoring data. Inembodiments, the ledger uses a blockchain structure to track records oftransactions for each of the at least one of the orders and the requestsfor service and parts. In embodiments, each record is stored as a blockin the blockchain structure.

In embodiments, a method comprises: generating, using one or morevibration sensors of a handheld device, vibration data representingmeasured vibrations of at least a portion of an industrial machine;mapping the vibration data to one or more severity units; and using theseverity units for predictive maintenance of the industrial machine bydetermining a maintenance action to perform on at least the portion ofan industrial machine based on the severity units. In embodiments,mapping the vibration data to one or more severity units comprises:mapping portions of the vibration data that have frequenciescorresponding to a below the low-end knee threshold-range of a vibrationfrequency spectra to first severity units; mapping portions of thevibration data that have frequencies corresponding to a mid-range of thevibration frequency spectra to second severity units; and mappingportions of the vibration data that have frequencies corresponding to anabove the high-end knee threshold-range of the vibration frequencyspectra to third severity units. In embodiments, the mapping of thevibration data to the one or more severity units is performed at thehandheld device. In embodiments, the mapping of the vibration data tothe one or more severity units is performed at a server. In embodiments,the method further comprises transmitting the vibration data from thehandheld device to the server. In embodiments, the method furthercomprises: detecting, using a collective processing mind associated withthe handheld device, that the handheld device is in near proximity tothe industrial machine; transmitting, from the collective processingmind, a first command to the handheld device to cause the handhelddevice to generate the vibration data; and, after the generating of thevibration data, transmitting, from the collective processing mind, asecond command to the handheld device to cause the handheld device totransmit the vibration data to the collective processing mind.

In embodiments, a system comprises: an industrial machine comprising atleast one vibration sensor disposed to capture vibration of a portion ofthe industrial machine; a mobile data collector that generates vibrationdata by collecting the captured vibration from the at least onevibration sensor; a multi-segment vibration frequency spectra structurethat facilitates mapping the captured vibration to one vibrationfrequency segment of the multiple segments of vibration frequency; aseverity unit algorithm that receives the determined frequency of thevibration and the corresponding mapped segment and produces a severityvalue which is then mapped to one of a plurality of severity unitsdefined for the corresponding mapped segment; and a signal generatingcircuit that receives the one of the plurality of severity units, andbased thereon, signals a predictive maintenance server to execute acorresponding maintenance action on the portion of the industrialmachine.

In embodiments, a method comprises: using a distributed ledger to trackone or more transactions executed in an automated data marketplace forindustrial Internet of Things data. In embodiments, the distributedledger distributes storage for data indicative of the one or moretransactions across one or more devices. In embodiments, the dataindicative of the one or more transactions corresponds to transactionrecords; and using one or more mobile data collectors to generate sensordata representative of a condition of an industrial machine. Inembodiments, the sensor data is used to determine at least one of ordersor requests for service and parts used to resolve an issue associatedwith the condition of the machine. In embodiments, a transaction recordstored in the distributed ledger represents one or more of the sensordata, the condition of the industrial machine, the at least one of theorders or the requests for service and parts, the issue associated withthe condition of the machine, or a hash used to identify the transactionrecord. In embodiments, the distributed ledger uses a blockchainstructure to store the transaction records. In embodiments, each of thetransaction records is stored as a block in the blockchain structure. Inembodiments, each mobile data collector is one of a mobile vehicle, amobile robot, a handheld device, or a wearable device. In embodiments,the method further comprises: applying machine fault detection andclassification algorithms to the sensor data to produce an industrialmachine service recommendation; and producing the at least one of theorders or the requests for service and parts based on the industrialmachine service recommendation. In embodiments, the one or more mobiledata collectors use a computer vision system to generate the sensor databy capturing raw image data using one or more data capture devices andprocessing the raw image data to generate image set data. Inembodiments, the image set data is used to produce the industrialmachine service recommendation.

In embodiments, a system comprises: an IoT network connecting anindustrial machine and one or more mobile data collectors, each mobiledata collector including one or more sensors for generating sensor dataindicative of conditions of the industrial machine; and a server incommunication with the IoT network, the server implementing a predictivemaintenance platform that uses a distributed ledger to track maintenancetransactions related to the industrial machine, the distributed ledgerstoring transaction records corresponding to the maintenancetransactions. In embodiments, the predictive maintenance platformdistributes at least some of the transaction records to the one or moremobile data collectors. In embodiments, the system further comprises aself-organizing storage system that optimizes storage of the transactionrecords within the distributed ledger. In embodiments, the systemfurther comprises a self-organizing storage system that optimizesstorage of maintenance data associated with the industrial machine. Inembodiments, the system further comprises a self-organizing storagesystem that optimizes storage of IoT data associated with the IoTnetwork. In embodiments, the system further comprises a self-organizingstorage system that optimizes storage of parts and service data relatedto the maintenance transactions. In embodiments, the system furthercomprises a self-organizing storage system that optimizes storage ofknowledge base data associated with the industrial machine. Inembodiments, each mobile data collector is one of a mobile vehicle, amobile robot, a handheld device, or a wearable device. In embodiments,the system further comprises an industrial machine predictivemaintenance facility that produces an industrial machine servicerecommendation for the condition by applying machine fault detection andclassification algorithms to the sensor data. In embodiments, the systemfurther comprises a severity unit algorithm that produces a severityvalue for the condition based on the sensor data. In embodiments, theindustrial machine service recommendation is produced based on theseverity value. In embodiments, at least one of the one or more mobiledata collectors use a computer vision system to generate the sensor databy capturing raw image data using one or more data capture devices andprocessing the raw image data to generate image set data. Inembodiments, the image set data is used to produce the industrialmachine service recommendation.

In embodiments, a method comprises: generating, using a mobile datacollector, sensor data representing a condition of an industrialmachine; determining a severity of the condition of the industrialmachine by analyzing the sensor data; predicting a maintenance action toperform against the industrial machine based on the severity of thecondition; and storing a transaction record of the predicted maintenanceaction within a ledger of service activity associated with theindustrial machine. In embodiments, the method further comprises:producing, in connection with the predicted maintenance action, at leastone of orders or requests for service and parts used to perform themaintenance action; and including data indicative of the at least one ofthe orders or requests for service and parts within the transactionrecord. In embodiments, the mobile data collector is one of a mobilevehicle, a mobile robot, a handheld device, or a wearable device. Inembodiments, the method further comprises applying machine learning todata representative of conditions of the industrial machine. Inembodiments, determining the severity of the sensor data by analyzingthe frequency of the vibrations comprises using the applied machinelearning to determine the severity of the sensor data based on machinelearning data associated with the at least one of the frequency or thevelocity of the vibrations.

In embodiments, an industrial machine predictive maintenance systemcomprises: a computer vision system that generates one or more imagedata sets using raw data captured by one or more data capture devicesand that detects an operating characteristic of an industrial machinebased on the one or more image data sets; an industrial machinepredictive maintenance facility that produces an industrial machineservice recommendation by applying machine fault detection andclassification algorithms to data indicative of the operatingcharacteristic; a computerized maintenance management system (CMMS) thatproduces at least one of orders and requests for service and partsresponsive to receiving the industrial machine service recommendation;and a service and delivery coordination facility that receives andprocesses information regarding services to perform on the industrialmachine based on the at least one of orders and requests for service andparts. In embodiments, the service and delivery coordination facilityvalidates the services to perform on the industrial machine whileproducing a ledger of service activity and results for the industrialmachine. In embodiments, the ledger uses a blockchain structure to trackrecords of transactions for each of the at least one of the orders andthe requests for service and parts. In embodiments, each record isstored as a block in the blockchain structure. In embodiments, the CMMSgenerates subsequent blocks of the ledger by combining data from atleast one of shipment readiness, installation, operational sensor data,service events, parts orders, service orders, or diagnostic activitywith a hash of a most recently generated block in the ledger. Inembodiments, the industrial machine predictive maintenance facilityproduces the industrial machine service recommendation using data storedwithin a knowledge base associated with the industrial machine. Inembodiments, the operating characteristic relates to vibrations detectedfor at least a portion of the industrial machine. In embodiments, theindustrial machine predictive maintenance facility produces theindustrial machine service recommendation according to a severity unitcalculated for the detected vibrations. In embodiments, the severityunit is calculated for the detected vibrations by determining afrequency of the detected vibrations, determining a segment of amulti-segment vibration frequency spectra that bounds the detectedvibrations, and calculating the severity unit for the detectedvibrations based on the determined segment. In embodiments, the segmentof a multi-segment vibration frequency spectra that bounds the detectedvibrations is determined by mapping the detected vibrations to one of anumber of severity units based on the determined segment. Inembodiments, each of the severity units corresponds to a different rangeof the multi-segment vibration frequency spectra. In embodiments, thedetected vibrations are mapped to a first severity unit when thefrequency of the captured vibration corresponds to a below a low-endknee threshold-range of the multi-segment vibration frequency spectra.In embodiments, the detected vibrations are mapped to a second severityunit when the frequency of the captured vibration corresponds to amid-range of the multi-segment vibration frequency spectra. Inembodiments, the detected vibrations are mapped to a third severity unitwhen the frequency of the captured vibration corresponds to an above thehigh-end knee threshold-range of the multi-segment vibration frequencyspectra. In embodiments, the severity unit indicates that the detectedvibrations may lead to a failure of at least the portion of theindustrial machine. In embodiments, the industrial machine servicerecommendation includes a recommendation for preventing or mitigatingthe failure. In embodiments, the at least one of the orders and therequests for service is for a part or a service used to prevent ormitigate the failure. In embodiments, the one or more data capturedevices are external to the computer vision system. In embodiments, theindustrial machine predictive maintenance system further comprises amobile data collector configured to perform a maintenance actioncorresponding to the industrial machine service recommendation on theindustrial machine by using the at least one of orders and requests forservice and parts. In embodiments, the service and delivery coordinationfacility receives a signal from the mobile data collector indicating aperformance of the maintenance action. In embodiments, the service anddelivery coordination facility uses a ledger to record service activityand results for the industrial machine. In embodiments, the service anddelivery coordination facility generates a new record in the ledgerbased on the signal received from the mobile data collector.

In embodiments, an industrial machine predictive maintenance systemcomprises: a computer vision system that generates one or more imagedata sets using raw data captured by one or more data capture devicesand that detects an operating characteristic of an industrial machinebased on the one or more image data sets; an industrial machinepredictive maintenance facility that produces an industrial machineservice recommendation by applying machine fault detection andclassification algorithms to data indicative of the operatingcharacteristic; and a computerized maintenance management system (CMMS)that produces at least one of orders and requests for service and partsresponsive to receiving the industrial machine service recommendation.In embodiments, the industrial machine predictive maintenance systemfurther comprises a service and delivery coordination facility thatreceives and processes information regarding services to perform on theindustrial machine based on the at least one of orders and requests forservice and parts. In embodiments, the service and delivery coordinationfacility validates the services to perform on the industrial machinewhile producing a ledger of service activity and results for theindustrial machine. In embodiments, the ledger uses a blockchainstructure to track records of transactions for each of the at least oneof the orders and the requests for service and parts. In embodiments,each record is stored as a block in the blockchain structure. Inembodiments, the CMMS generates subsequent blocks of the ledger bycombining data from at least one of shipment readiness, installation,operational sensor data, service events, parts orders, service orders,or diagnostic activity with a hash of a most recently generated block inthe ledger. In embodiments, the industrial machine predictivemaintenance facility produces the industrial machine servicerecommendation using data stored within a knowledge base associated withthe industrial machine. In embodiments, the operating characteristicrelates to vibrations detected for at least a portion of the industrialmachine. In embodiments, the industrial machine predictive maintenancefacility produces the industrial machine service recommendationaccording to a severity unit calculated for the detected vibrations. Inembodiments, the severity unit is calculated for the detected vibrationsby determining a frequency of the detected vibrations, determining asegment of a multi-segment vibration frequency spectra that bounds thedetected vibrations, and calculating the severity unit for the detectedvibrations based on the determined segment. In embodiments, the segmentof a multi-segment vibration frequency spectra that bounds the detectedvibrations is determined by mapping the detected vibrations to one of anumber of severity units based on the determined segment. Inembodiments, each of the severity units corresponds to a different rangeof the multi-segment vibration frequency spectra. In embodiments, thedetected vibrations are mapped to a first severity unit when thefrequency of the captured vibration corresponds to a below a low-endknee threshold-range of the multi-segment vibration frequency spectra.In embodiments, the detected vibrations are mapped to a second severityunit when the frequency of the captured vibration corresponds to amid-range of the multi-segment vibration frequency spectra. Inembodiments, the detected vibrations are mapped to a third severity unitwhen the frequency of the captured vibration corresponds to an above thehigh-end knee threshold-range of the multi-segment vibration frequencyspectra. In embodiments, the severity unit indicates that the detectedvibrations may lead to a failure of at least the portion of theindustrial machine. In embodiments, the industrial machine servicerecommendation includes a recommendation for preventing or mitigatingthe failure. In embodiments, the at least one of the orders and therequests for service is for a part or a service used to prevent ormitigate the failure. In embodiments, the one or more data capturedevices are external to the computer vision system. In embodiments, theindustrial machine predictive maintenance system further comprises amobile data collector configured to perform a maintenance actioncorresponding to the industrial machine service recommendation on theindustrial machine by using the at least one of orders and requests forservice and parts. In embodiments, the service and delivery coordinationfacility receives a signal from the mobile data collector indicating aperformance of the maintenance action. In embodiments, the service anddelivery coordination facility uses a ledger to record service activityand results for the industrial machine. In embodiments, the service anddelivery coordination facility generates a new record in the ledgerbased on the signal received from the mobile data collector. Inembodiments, the mobile data collector is a mobile vehicle. Inembodiments, the mobile data collector is a mobile robot. Inembodiments, the mobile data collector is a handheld device. Inembodiments, the mobile data collector is a wearable device.

In embodiments, an industrial machine predictive maintenance systemcomprises: a computer vision system that generates one or more imagedata sets using raw data captured by one or more data capture devicesand that detects an operating characteristic of an industrial machinebased on the one or more image data sets; an industrial machinepredictive maintenance facility that produces an industrial machineservice recommendation based on the operating characteristic; and amobile data collector configured to perform a maintenance actioncorresponding to the industrial machine service recommendation on theindustrial machine. In embodiments, the mobile data collector is onemobile data collector of a swarm of mobile data collectors and theindustrial machine predictive maintenance system further comprises aself-organization system of the mobile data collector swarm thatcontrols movements of the mobile data collectors of the swarm within anindustrial environment that includes the industrial machine. Inembodiments, the industrial machine predictive maintenance facilityproduces the industrial machine service recommendation by applyingmachine fault detection and classification algorithms to data indicativeof the operating characteristic. In embodiments, the industrial machinepredictive maintenance facility produces the industrial machine servicerecommendation using data stored within a knowledge base associated withthe industrial machine. In embodiments, the operating characteristicrelates to vibrations detected for at least a portion of the industrialmachine. In embodiments, the industrial machine predictive maintenancefacility produces the industrial machine service recommendationaccording to a severity unit calculated for the detected vibrations. Inembodiments, the severity unit is calculated for the detected vibrationsby determining a frequency of the detected vibrations, determining asegment of a multi-segment vibration frequency spectra that bounds thedetected vibrations, and calculating the severity unit for the detectedvibrations based on the determined segment. In embodiments, the segmentof a multi-segment vibration frequency spectra that bounds the detectedvibrations is determined by mapping the detected vibrations to one of anumber of severity units based on the determined segment. Inembodiments, each of the severity units corresponds to a different rangeof the multi-segment vibration frequency spectra. In embodiments, thedetected vibrations are mapped to a first severity unit when thefrequency of the captured vibration corresponds to a below a low-endknee threshold-range of the multi-segment vibration frequency spectra.In embodiments, the detected vibrations are mapped to a second severityunit when the frequency of the captured vibration corresponds to amid-range of the multi-segment vibration frequency spectra. Inembodiments, the detected vibrations are mapped to a third severity unitwhen the frequency of the captured vibration corresponds to an above thehigh-end knee threshold-range of the multi-segment vibration frequencyspectra. In embodiments, the severity unit indicates that the detectedvibrations may lead to a failure of at least the portion of theindustrial machine. In embodiments, the industrial machine servicerecommendation includes a recommendation for preventing or mitigatingthe failure. In embodiments, the industrial machine predictivemaintenance system further comprises a computerized maintenancemanagement system (CMMS) that produces at least one of orders andrequests for service and parts responsive to receiving the industrialmachine service recommendation. In embodiments, the mobile datacollector performs the maintenance action by using the at least one oforders and requests for service and parts. In embodiments, theindustrial machine predictive maintenance system further comprises aservice and delivery coordination facility that receives and processesinformation regarding services to perform on the industrial machinebased on the at least one of orders and requests for service and parts.In embodiments, the service and delivery coordination facility validatesthe services to perform on the industrial machine while producing aledger of service activity and results for the industrial machine. Inembodiments, the ledger uses a blockchain structure to track records oftransactions for each of the at least one of the orders and the requestsfor service and parts. In embodiments, each record is stored as a blockin the blockchain structure. In embodiments, the CMMS generatessubsequent blocks of the ledger by combining data from at least one ofshipment readiness, installation, operational sensor data, serviceevents, parts orders, service orders, or diagnostic activity with a hashof a most recently generated block in the ledger.

In embodiments, a method for industrial machine predictive maintenancecomprises: generating data representing a condition of an industrialmachine using one or more sensors of a mobile data collector; processingthe data to determine a severity of the condition of the industrialmachine; determining an industrial machine service recommendation forthe condition of the industrial machine based on the severity; andgenerating a signal indicative of the industrial machine servicerecommendation. In embodiments, the mobile data collector uses acomputer vision system that generates, as the data, one or more imagedata sets using raw data captured by one or more data capture devicesand that detects an operating characteristic of an industrial machinebased on the one or more image data sets. In embodiments, the operatingcharacteristic corresponds to the condition of the industrial machine.In embodiments, the mobile data collector is a mobile robot. Inembodiments, the mobile data collector is a mobile vehicle. Inembodiments, the mobile data collector is a handheld device. Inembodiments, the mobile data collector is a wearable device. Inembodiments, determining the industrial machine service recommendationfor the condition of the industrial machine based on the severitycomprises using an intelligent system to apply machine fault detectionand classification algorithms to the data and the severity. Inembodiments, the condition of the industrial machine relates tovibrations detected for at least a portion of the industrial machine,and processing the data to determine the severity of the condition ofthe industrial machine comprises: determining a frequency of thedetected vibrations; determining a segment of a multi-segment vibrationfrequency spectra that bounds the detected vibrations; and calculatingthe severity for the detected vibrations based on the determinedsegment. In embodiments, the severity corresponds to a severity unit. Inembodiments, the segment of a multi-segment vibration frequency spectrathat bounds the detected vibrations is determined by mapping thedetected vibrations to one of a number of severity units based on thedetermined segment. In embodiments, each of the severity unitscorresponds to a different range of the multi-segment vibrationfrequency spectra. In embodiments, the method further comprises mappingthe detected vibrations to a first severity unit when the frequency ofthe detected vibrations corresponds to a below a low-end kneethreshold-range of the multi-segment vibration frequency spectra;mapping the detected vibrations to a second severity unit when thefrequency of the detected vibrations corresponds to a mid-range of themulti-segment vibration frequency spectra; and mapping the detectedvibrations to a third severity unit when the frequency of the detectedvibrations corresponds to an above the high-end knee threshold-range ofthe multi-segment vibration frequency spectra. In embodiments, themethod further comprises transmitting the signal to a mobile robotconfigured to perform a maintenance action associated with theindustrial machine service recommendation. In embodiments, the methodfurther comprises storing a record of the industrial machine servicerecommendation within a ledger of service activity associated with theindustrial machine. In embodiments, the ledger uses a blockchainstructure to track records of industrial machine service recommendationsfor the industrial machine. In embodiments, each record is stored as ablock in the blockchain structure. In embodiments, the method furthercomprises producing at least one of orders or requests for service andparts based on the industrial machine service recommendation. Inembodiments, the signal indicates the at least one of the orders or therequests for service and parts.

In embodiments, a method for industrial machine predictive maintenancecomprises: generating data representing a condition of an industrialmachine using one or more wearable devices, each wearable deviceincluding one or more sensors. In embodiments, a wearable device of theone or more wearable devices generates some or all of the data when thewearable device is in near proximity to the industrial machine;processing the data to determine a severity of the condition of theindustrial machine; determining an industrial machine servicerecommendation for the condition of the industrial machine based on theseverity; and storing a record of the industrial machine servicerecommendation within a ledger of service activity associated with theindustrial machine. In embodiments, the condition of the industrialmachine relates to vibrations detected for at least a portion of theindustrial machine, and processing the data to determine the severity ofthe condition of the industrial machine comprises: determining afrequency of the detected vibrations; determining a segment of amulti-segment vibration frequency spectra that bounds the detectedvibrations; and calculating the severity for the detected vibrationsbased on the determined segment. In embodiments, the severitycorresponds to a severity unit. In embodiments, the segment of amulti-segment vibration frequency spectra that bounds the detectedvibrations is determined by mapping the detected vibrations to one of anumber of severity units based on the determined segment. Inembodiments, each of the severity units corresponds to a different rangeof the multi-segment vibration frequency spectra. In embodiments, themethod further comprises: mapping the detected vibrations to a firstseverity unit when the frequency of the detected vibrations correspondsto a below a low-end knee threshold-range of the multi-segment vibrationfrequency spectra; mapping the detected vibrations to a second severityunit when the frequency of the detected vibrations corresponds to amid-range of the multi-segment vibration frequency spectra; and mappingthe detected vibrations to a third severity unit when the frequency ofthe detected vibrations corresponds to an above the high-end kneethreshold-range of the multi-segment vibration frequency spectra. Inembodiments, determining the industrial machine service recommendationfor the condition of the industrial machine based on the severitycomprises using an intelligent system to apply machine fault detectionand classification algorithms to the data and the severity. Inembodiments, the intelligent system includes a you only look once neuralnetwork. In embodiments, the intelligent system includes a you only lookonce convolutional neural network. In embodiments, the intelligentsystem includes a set of neural networks configured to operate on orfrom a field programmable gate array. In embodiments, the intelligentsystem includes a set of neural networks configured to operate on orfrom a field programmable gate array and graphics processing unit hybridcomponent. In embodiments, the intelligent system includes userconfigurable series and parallel flow for a hybrid neural network. Inembodiments, the intelligent system includes a machine learning systemfor configuring a topology or workflow for a set of neural networks. Inembodiments, the intelligent system includes a deep learning system forconfiguring a topology or workflow for a set of neural networks. Inembodiments, the ledger uses a blockchain structure to track records ofindustrial machine service recommendations for the industrial machine.In embodiments, each record is stored as a block in the blockchainstructure. In embodiments, the method further comprises: producing atleast one of orders or requests for service and parts based on theindustrial machine service recommendation. In embodiments, the recordfor the industrial machine service recommendation stored in the ledgerindicates the at least one of the orders or the requests for service andparts. In embodiments, the one or more wearable devices are integratedwithin an industrial uniform. In embodiments, the wearable device isintegrated within an article of clothing. In embodiments, the wearabledevice is integrated within an accessory article.

In embodiments, a method for industrial machine predictive maintenancecomprises: generating data representing a condition of an industrialmachine using one or more handheld devices, each handheld deviceincluding one or more sensors. In embodiments, a handheld device of theone or more handheld devices generates some or all of the data when thehandheld device is in near proximity to the industrial machine;processing the data to determine a severity of the condition of theindustrial machine; determining an industrial machine servicerecommendation for the condition of the industrial machine based on theseverity; and storing a record of the industrial machine servicerecommendation within a ledger of service activity associated with theindustrial machine. In embodiments, the condition of the industrialmachine relates to vibrations detected for at least a portion of theindustrial machine, and processing the data to determine the severity ofthe condition of the industrial machine comprises: determining afrequency of the detected vibrations; determining a segment of amulti-segment vibration frequency spectra that bounds the detectedvibrations; and calculating the severity for the detected vibrationsbased on the determined segment. In embodiments, the severitycorresponds to a severity unit. In embodiments, the segment of amulti-segment vibration frequency spectra that bounds the detectedvibrations is determined by mapping the detected vibrations to one of anumber of severity units based on the determined segment. Inembodiments, each of the severity units corresponds to a different rangeof the multi-segment vibration frequency spectra. In embodiments, themethod further comprises: mapping the detected vibrations to a firstseverity unit when the frequency of the detected vibrations correspondsto a below a low-end knee threshold-range of the multi-segment vibrationfrequency spectra; mapping the detected vibrations to a second severityunit when the frequency of the detected vibrations corresponds to amid-range of the multi-segment vibration frequency spectra; and mappingthe detected vibrations to a third severity unit when the frequency ofthe detected vibrations corresponds to an above the high-end kneethreshold-range of the multi-segment vibration frequency spectra. Inembodiments, determining the industrial machine service recommendationfor the condition of the industrial machine based on the severitycomprises using an intelligent system to apply machine fault detectionand classification algorithms to the data and the severity. Inembodiments, the intelligent system includes a you only look once neuralnetwork. In embodiments, the intelligent system includes a you only lookonce convolutional neural network. In embodiments, the intelligentsystem includes a set of neural networks configured to operate on orfrom a field programmable gate array. In embodiments, the intelligentsystem includes a set of neural networks configured to operate on orfrom a field programmable gate array and graphics processing unit hybridcomponent. In embodiments, the intelligent system includes userconfigurable series and parallel flow for a hybrid neural network. Inembodiments, the intelligent system includes a machine learning systemfor configuring a topology or workflow for a set of neural networks. Inembodiments, the intelligent system includes a deep learning system forconfiguring a topology or workflow for a set of neural networks. Inembodiments, the ledger uses a blockchain structure to track records ofindustrial machine service recommendations for the industrial machine.In embodiments, each record is stored as a block in the blockchainstructure. In embodiments, the method further comprises producing atleast one of orders or requests for service and parts based on theindustrial machine service recommendation. In embodiments, the recordfor the industrial machine service recommendation stored in the ledgerindicates the at least one of the orders or the requests for service andparts.

In embodiments, a method for industrial machine predictive maintenancecomprises: generating data representing a condition of an industrialmachine using one or more mobile robots, each mobile robot including oneor more sensors. In embodiments, a mobile robot of the one or moremobile robots generates some or all of the data when the mobile robot isin near proximity to the industrial machine; processing the data todetermine a severity of the condition of the industrial machine;determining an industrial machine service recommendation for thecondition of the industrial machine based on the severity; and storing arecord of the industrial machine service recommendation within a ledgerof service activity associated with the industrial machine. Inembodiments, the condition of the industrial machine relates tovibrations detected for at least a portion of the industrial machine,and processing the data to determine the severity of the condition ofthe industrial machine comprises: determining a frequency of thedetected vibrations; determining a segment of a multi-segment vibrationfrequency spectra that bounds the detected vibrations; and calculatingthe severity for the detected vibrations based on the determinedsegment. In embodiments, the severity corresponds to a severity unit. Inembodiments, the segment of a multi-segment vibration frequency spectrathat bounds the detected vibrations is determined by mapping thedetected vibrations to one of a number of severity units based on thedetermined segment. In embodiments, each of the severity unitscorresponds to a different range of the multi-segment vibrationfrequency spectra. In embodiments, the method further comprises mappingthe detected vibrations to a first severity unit when the frequency ofthe detected vibrations corresponds to a below a low-end kneethreshold-range of the multi-segment vibration frequency spectra;mapping the detected vibrations to a second severity unit when thefrequency of the detected vibrations corresponds to a mid-range of themulti-segment vibration frequency spectra; and mapping the detectedvibrations to a third severity unit when the frequency of the detectedvibrations corresponds to an above the high-end knee threshold-range ofthe multi-segment vibration frequency spectra. In embodiments,determining the industrial machine service recommendation for thecondition of the industrial machine based on the severity comprisesusing an intelligent system to apply machine fault detection andclassification algorithms to the data and the severity. In embodiments,the intelligent system includes a you only look once neural network. Inembodiments, the intelligent system includes a you only look onceconvolutional neural network. In embodiments, the intelligent systemincludes a set of neural networks configured to operate on or from afield programmable gate array. In embodiments, the intelligent systemincludes a set of neural networks configured to operate on or from afield programmable gate array and graphics processing unit hybridcomponent. In embodiments, the intelligent system includes userconfigurable series and parallel flow for a hybrid neural network. Inembodiments, the intelligent system includes a machine learning systemfor configuring a topology or workflow for a set of neural networks. Inembodiments, the intelligent system includes a deep learning system forconfiguring a topology or workflow for a set of neural networks. Inembodiments, the ledger uses a blockchain structure to track records ofindustrial machine service recommendations for the industrial machine.In embodiments, each record is stored as a block in the blockchainstructure. In embodiments, the method further comprises producing atleast one of orders or requests for service and parts based on theindustrial machine service recommendation. In embodiments, the recordfor the industrial machine service recommendation stored in the ledgerindicates the at least one of the orders or the requests for service andparts. In embodiments, the mobile robot is one of a plurality of mobilerobots of a mobile data collector swarm. In embodiments, the methodfurther comprises controlling the mobile data collector swarm to causethe mobile robot to approach a location of the industrial machine withinan industrial environment. In embodiments, controlling the mobile datacollector swarm to cause the mobile robot to approach a location of theindustrial machine within an industrial environment comprises usingself-organization systems of the mobile data collector swarm to controlmovements of the mobile robot within the industrial environment based onlocations of other mobile robots of the mobile data collector swarmwithin the industrial environment.

In embodiments, a method for industrial machine predictive maintenancecomprises: generating data representing a condition of an industrialmachine using one or more mobile vehicles, each mobile vehicle includingone or more sensors. In embodiments, a mobile vehicle of the one or moremobile vehicles generates some or all of the data when the mobilevehicle is in near proximity to the industrial machine; processing thedata to determine a severity of the condition of the industrial machine;determining an industrial machine service recommendation for thecondition of the industrial machine based on the severity; and storing arecord of the industrial machine service recommendation within a ledgerof service activity associated with the industrial machine. Inembodiments, the condition of the industrial machine relates tovibrations detected for at least a portion of the industrial machine,and processing the data to determine the severity of the condition ofthe industrial machine comprises: determining a frequency of thedetected vibrations; determining a segment of a multi-segment vibrationfrequency spectra that bounds the detected vibrations; and calculatingthe severity for the detected vibrations based on the determinedsegment. In embodiments, the severity corresponds to a severity unit. Inembodiments, the segment of a multi-segment vibration frequency spectrathat bounds the detected vibrations is determined by mapping thedetected vibrations to one of a number of severity units based on thedetermined segment. In embodiments, each of the severity unitscorresponds to a different range of the multi-segment vibrationfrequency spectra. In embodiments, the method further comprises: mappingthe detected vibrations to a first severity unit when the frequency ofthe detected vibrations corresponds to a below a low-end kneethreshold-range of the multi-segment vibration frequency spectra;mapping the detected vibrations to a second severity unit when thefrequency of the detected vibrations corresponds to a mid-range of themulti-segment vibration frequency spectra; and mapping the detectedvibrations to a third severity unit when the frequency of the detectedvibrations corresponds to an above the high-end knee threshold-range ofthe multi-segment vibration frequency spectra. In embodiments,determining the industrial machine service recommendation for thecondition of the industrial machine based on the severity comprisesusing an intelligent system to apply machine fault detection andclassification algorithms to the data and the severity. In embodiments,the intelligent system includes a you only look once neural network. Inembodiments, the intelligent system includes a you only look onceconvolutional neural network. In embodiments, the intelligent systemincludes a set of neural networks configured to operate on or from afield programmable gate array. In embodiments, the intelligent systemincludes a set of neural networks configured to operate on or from afield programmable gate array and graphics processing unit hybridcomponent. In embodiments, the intelligent system includes userconfigurable series and parallel flow for a hybrid neural network.

In embodiments, the intelligent system includes a machine learningsystem for configuring a topology or workflow for a set of neuralnetworks. In embodiments, the intelligent system includes a deeplearning system for configuring a topology or workflow for a set ofneural networks. In embodiments, the ledger uses a blockchain structureto track records of industrial machine service recommendations for theindustrial machine. In embodiments, each record is stored as a block inthe blockchain structure. In embodiments, the method further comprisesproducing at least one of orders or requests for service and parts basedon the industrial machine service recommendation. In embodiments, therecord for the industrial machine service recommendation stored in theledger indicates the at least one of the orders or the requests forservice and parts. In embodiments, the mobile vehicle is one of aplurality of mobile vehicles of a mobile data collector swarm. Inembodiments, the method further comprises controlling the mobile datacollector swarm to cause the mobile vehicle to approach a location ofthe industrial machine within an industrial environment. In embodiments,controlling the mobile data collector swarm to cause the mobile vehicleto approach a location of the industrial machine within an industrialenvironment comprises using self-organization systems of the mobile datacollector swarm to control movements of the mobile vehicle within theindustrial environment based on locations of other mobile vehicles ofthe mobile data collector swarm within the industrial environment.

In embodiments, a method comprises: training a computer vision system todetect conditions of industrial machines using a training data setcomprising at least one of image data or non-image data; detecting acondition of an industrial machine using the trained computer vision andbased on a data set generated using one or more data capture devices;determining a severity value for the detected condition, the severityrepresenting an impact of the detected condition on the industrialmachine; producing, based on the severity value, at least one of ordersor requests for service and parts to use to resolve an issue related tothe detected condition of the industrial machine; and storing a recordof the issue related to the detected condition of the industrial machinewithin a ledger associated with the industrial machine. In embodiments,the one or more data capture devices includes a radiation imagingdevice, a sonic capture device, a LIDAR device, a point cloud capturedevice, or an infrared inspection device. In embodiments, the detectedcondition is detected based on vibration characteristics of theindustrial machine. In embodiments, the detected condition is detectedbased on pressure characteristics of the industrial machine. Inembodiments, the detected condition is detected based on temperaturecharacteristics of the industrial machine. In embodiments, the detectedcondition is detected based on chemical characteristics of theindustrial machine. In embodiments, training the computer vision systemto detect the conditions of the industrial machines using the trainingdata set comprising the at least one of image data or non-image datacomprises: using a deep learning system to detect features from the atleast one of the image data or non-image data; and using the detectedfeatures to train a classification model to learn to detect theconditions of the industrial machines based on characteristics of thedetected features and based on outcome feedback. In embodiments, theoutcome feedback relates to at least one of maintenance, repair, uptime,downtime, profitability, efficiency, or operational optimization of theindustrial machines, of processes for using the industrial machines, orof facilities including the industrial machines. In embodiments,detecting the condition of the industrial machine using the trainedcomputer vision and based on the data set generated using the one ormore data capture devices comprises using part recognition to identifyone or more components of the industrial machine that will lead to theissue related to the detected condition. In embodiments, the at leastone of the orders or the requests for service and parts is forreplacement parts for the one or more components. In embodiments, the atleast one of the orders or the requests for service and parts is notproduced when the severity value does not meet a threshold. Inembodiments, the method further comprises using a predictive maintenanceknowledge system to update a predictive maintenance knowledge baseaccording to at least one of the detected condition, the at least one ofthe orders or the requests for service and parts, or the stored recordin the ledger.

In embodiments, a system comprises: a computerized maintenancemanagement system (CMMS) that produces at least one of orders orrequests for service and parts responsive to receiving an industrialmachine service recommendation corresponding to an industrial machineand that generates a signal indicative of the produced at least one ofthe orders or requests for service and parts; and a mobile datacollector that receives the signal and indicates the industrial machineservice recommendation or the produced at least one of the orders orrequests for service and parts to a worker who uses the mobile datacollector. In embodiments, the mobile data collector is a wearabledevice. In embodiments, the wearable device indicates the industrialmachine service recommendation or the produced at least one of theorders or requests for service and parts to the worker by outputtingdata indicative of the industrial machine service recommendation or theproduced at least one of the orders or requests for service and parts toa display of the wearable device. In embodiments, the mobile datacollector is a handheld device. In embodiments, the handheld deviceindicates the industrial machine service recommendation or the producedat least one of the orders or requests for service and parts to theworker by outputting data indicative of the industrial machine servicerecommendation or the produced at least one of the orders or requestsfor service and parts to a display of the handheld device. Inembodiments, the system further comprises a service and deliverycoordination facility that receives and processes information regardingservices performed on the industrial machine responsive to the at leastone of orders or requests for service and parts, thereby validating theservices performed while producing a ledger of service activity andresults for the industrial machine. In embodiments, the system furthercomprises a self-organizing data collector that causes a new record tobe stored in the ledger, the new record indicating at least one of theindustrial machine service recommendation or the produced at least oneof the orders or requests for service and parts. In embodiments, theledger uses a blockchain structure to track records of transactions foreach of the at least one of the orders and the requests for service andparts. In embodiments, each record is stored as a block in theblockchain structure. In embodiments, the CMMS generates subsequentblocks of the ledger by combining data from at least one of shipmentreadiness, installation, operational sensor data, service events, partsorders, service orders, or diagnostic activity with a hash of a mostrecently generated block in the ledger.

In embodiments, a system comprises: a computerized maintenancemanagement system (CMMS) that produces at least one of orders orrequests for service and parts responsive to receiving an industrialmachine service recommendation corresponding to an industrial machineand that generates a signal indicative of the produced at least one ofthe orders or requests for service and parts; a mobile data collectorthat receives the signal and indicates the industrial machine servicerecommendation or the produced at least one of the orders or requestsfor service and parts to a worker who uses the mobile data collector;and a service and delivery coordination facility that receives andprocesses information regarding services performed on the industrialmachine responsive to the at least one of orders or requests for serviceand parts, thereby validating the services performed while producing aledger of service activity and results for the industrial machine. Inembodiments, the mobile data collector is a wearable device. Inembodiments, the wearable device indicates the industrial machineservice recommendation or the produced at least one of the orders orrequests for service and parts to the worker by outputting dataindicative of the industrial machine service recommendation or theproduced at least one of the orders or requests for service and parts toa display of the wearable device. The system of claim 1016. Inembodiments, the mobile data collector is a handheld device. Inembodiments, the handheld device indicates the industrial machineservice recommendation or the produced at least one of the orders orrequests for service and parts to the worker by outputting dataindicative of the industrial machine service recommendation or theproduced at least one of the orders or requests for service and parts toa display of the handheld device. In embodiments, the system furthercomprises a self-organizing data collector that causes a new record tobe stored in the ledger, the new record indicating at least one of theindustrial machine service recommendation or the produced at least oneof the orders or requests for service and parts. In embodiments, theledger uses a blockchain structure to track records of transactions foreach of the at least one of the orders and the requests for service andparts. In embodiments, each record is stored as a block in theblockchain structure. In embodiments, the CMMS generates subsequentblocks of the ledger by combining data from at least one of shipmentreadiness, installation, operational sensor data, service events, partsorders, service orders, or diagnostic activity with a hash of a mostrecently generated block in the ledger.

In embodiments, a system comprises: a computerized maintenancemanagement system (CMMS) that produces at least one of orders orrequests for service and parts responsive to receiving an industrialmachine service recommendation corresponding to an industrial machineand that generates a signal indicative of the produced at least one ofthe orders or requests for service and parts; a mobile data collectorthat receives the signal and indicates the industrial machine servicerecommendation or the produced at least one of the orders or requestsfor service and parts to a worker who uses the mobile data collector;and a self-organizing data collector that causes a new record to bestored in the ledger, the new record indicating at least one of theindustrial machine service recommendation or the produced at least oneof the orders or requests for service and parts. In embodiments, theledger uses a blockchain structure to track records of transactions foreach of the at least one of the orders and the requests for service andparts. In embodiments, each record is stored as a block in theblockchain structure. In embodiments, the mobile data collector is awearable device. In embodiments, the wearable device indicates theindustrial machine service recommendation or the produced at least oneof the orders or requests for service and parts to the worker byoutputting data indicative of the industrial machine servicerecommendation or the produced at least one of the orders or requestsfor service and parts to a display of the wearable device. Inembodiments, the mobile data collector is a handheld device. Inembodiments, the handheld device indicates the industrial machineservice recommendation or the produced at least one of the orders orrequests for service and parts to the worker by outputting dataindicative of the industrial machine service recommendation or theproduced at least one of the orders or requests for service and parts toa display of the handheld device. In embodiments, the system furthercomprises a self-organizing data collector that causes a new record tobe stored in the ledger, the new record indicating at least one of theindustrial machine service recommendation or the produced at least oneof the orders or requests for service and parts. In embodiments, theCMMS generates subsequent blocks of the ledger by combining data from atleast one of shipment readiness, installation, operational sensor data,service events, parts orders, service orders, or diagnostic activitywith a hash of a most recently generated block in the ledger.

In embodiments, a method, comprises: detecting an operatingcharacteristic of an industrial machine using one or more sensors of amobile data collector; transmitting data indicative of the operatingcharacteristic to a server over a network; using intelligent systemsassociated with the server to process the operating characteristicagainst pre-recorded data for the industrial machine. In embodiments,processing the operating characteristic against the pre-recorded datafor the industrial machine includes identifying the pre-recorded datafor the industrial machine within a knowledge base associated with theindustrial environment; identifying, as a condition of the industrialmachine, a characteristic indicated by the pre-recorded data for theindustrial machine within the knowledge base; determining a severity ofthe condition, the severity representing an impact of the condition onthe industrial machine; predicting a maintenance action to performagainst the industrial machine based on the severity of the condition;and storing a transaction record of the predicted maintenance actionwithin a ledger of service activity associated with the industrialmachine. In embodiments, the mobile data collector is a mobile robot. Inembodiments, the mobile data collector is a mobile vehicle. Inembodiments, the mobile data collector is a handheld device. Inembodiments, the mobile data collector is a wearable device. Inembodiments, the condition of the industrial machine relates tovibrations detected for at least a portion of the industrial machine,and determining the severity of the condition comprises: determining afrequency of the vibrations; determining a segment of a multi-segmentvibration frequency spectra that bounds the vibrations; and calculatingthe severity for the detected vibrations based on the determinedsegment. In embodiments, the severity corresponds to a severity unit. Inembodiments, the segment of a multi-segment vibration frequency spectrathat bounds the vibrations is determined by mapping the vibrations toone of a number of severity units based on the determined segment. Inembodiments, each of the severity units corresponds to a different rangeof the multi-segment vibration frequency spectra. In embodiments, themethod further comprises: mapping the vibrations to a first severityunit when the frequency of the vibrations corresponds to a below alow-end knee threshold-range of the multi-segment vibration frequencyspectra; mapping the vibrations to a second severity unit when thefrequency of the vibrations corresponds to a mid-range of themulti-segment vibration frequency spectra; and mapping the vibrations toa third severity unit when the frequency of the vibrations correspondsto an above the high-end knee threshold-range of the multi-segmentvibration frequency spectra. In embodiments, the ledger uses ablockchain structure to track transaction records for predictedmaintenance actions for the industrial machine. In embodiments, each ofthe transaction records is stored as a block in the blockchainstructure. In embodiments, the condition of the industrial machinerelates to a temperature detected for at least a portion of theindustrial machine. In embodiments, the condition of the industrialmachine relates to an electrical output detected for at least a portionof the industrial machine. In embodiments, the condition of theindustrial machine relates to a magnetic output detected for at least aportion of the industrial machine. In embodiments, the condition of theindustrial machine relates to a sound output detected for at least aportion of the industrial machine.

In embodiments, a method, comprises: detecting an operatingcharacteristic of an industrial machine using one or more sensors of amobile data collector; transmitting data indicative of the operatingcharacteristic to a server over a network; using intelligent systemsassociated with the server to process the operating characteristicagainst pre-recorded data for the industrial machine. In embodiments,processing the operating characteristic against the pre-recorded datafor the industrial machine includes identifying the pre-recorded datafor the industrial machine within a knowledge base associated with theindustrial environment; identifying, as a condition of the industrialmachine, a characteristic indicated by the pre-recorded data for theindustrial machine within the knowledge base, the condition of theindustrial machine relating to vibrations detected for at least aportion of the industrial machine; determining a severity of thecondition, the severity representing an impact of the condition on theindustrial machine, based on a segment of a multi-segment vibrationfrequency spectra that bounds the vibrations; and predicting amaintenance action to perform against the industrial machine based onthe severity of the condition. In embodiments, the mobile data collectoris a mobile robot. In embodiments, the mobile data collector is a mobilevehicle. In embodiments, the mobile data collector is a handheld device.In embodiments, the mobile data collector is a wearable device. Inembodiments, the severity corresponds to a severity unit. Inembodiments, the segment of a multi-segment vibration frequency spectrathat bounds the vibrations is determined by mapping the vibrations toone of a number of severity units based on the determined segment. Inembodiments, each of the severity units corresponds to a different rangeof the multi-segment vibration frequency spectra. In embodiments, themethod further comprises: mapping the vibrations to a first severityunit when the frequency of the vibrations corresponds to a below alow-end knee threshold-range of the multi-segment vibration frequencyspectra; mapping the vibrations to a second severity unit when thefrequency of the vibrations corresponds to a mid-range of themulti-segment vibration frequency spectra; and mapping the vibrations toa third severity unit when the frequency of the vibrations correspondsto an above the high-end knee threshold-range of the multi-segmentvibration frequency spectra. In embodiments, the method furthercomprises storing a transaction record of the predicted maintenanceaction within a ledger of service activity associated with theindustrial machine. In embodiments, the ledger uses a blockchainstructure to track transaction records for predicted maintenance actionsfor the industrial machine. In embodiments, each of the transactionrecords is stored as a block in the blockchain structure.

In embodiments, a method comprises: detecting an operatingcharacteristic of an industrial machine using one or more sensors of amobile data collector, the operating characteristic of the industrialmachine relating to vibrations detected for at least a portion of theindustrial machine; determining a severity of the operatingcharacteristic, the severity representing an impact of the operatingcharacteristic on the industrial machine, based on a segment of amulti-segment vibration frequency spectra that bounds the vibrations;and predicting a maintenance action to perform against the industrialmachine based on the severity of the operating characteristic. Inembodiments, the mobile data collector is a mobile robot. Inembodiments, the mobile data collector is a mobile vehicle. Inembodiments, the mobile data collector is a handheld device. Inembodiments, the mobile data collector is a wearable device. Inembodiments, the severity corresponds to a severity unit. Inembodiments, the segment of a multi-segment vibration frequency spectrathat bounds the vibrations is determined by mapping the vibrations toone of a number of severity units based on the determined segment. Inembodiments, each of the severity units corresponds to a different rangeof the multi-segment vibration frequency spectra. In embodiments, themethod further comprises: mapping the vibrations to a first severityunit when the frequency of the vibrations corresponds to a below alow-end knee threshold-range of the multi-segment vibration frequencyspectra; mapping the vibrations to a second severity unit when thefrequency of the vibrations corresponds to a mid-range of themulti-segment vibration frequency spectra; and mapping the vibrations toa third severity unit when the frequency of the vibrations correspondsto an above the high-end knee threshold-range of the multi-segmentvibration frequency spectra. In embodiments, the method furthercomprises storing a transaction record of the predicted maintenanceaction within a ledger of service activity associated with theindustrial machine. In embodiments, the ledger uses a blockchainstructure to track transaction records for predicted maintenance actionsfor the industrial machine. In embodiments, each of the transactionrecords is stored as a block in the blockchain structure.

In embodiments, a method comprises: detecting an operatingcharacteristic of an industrial machine using one or more sensors of amobile data collector, the operating characteristic of the industrialmachine relating to vibrations detected for at least a portion of theindustrial machine; determining a severity of the operatingcharacteristic, the severity representing an impact of the operatingcharacteristic on the industrial machine, based on a segment of amulti-segment vibration frequency spectra that bounds the vibrations;predicting a maintenance action to perform against the industrialmachine based on the severity of the operating characteristic; andstoring a transaction record of the predicted maintenance action withina ledger of service activity associated with the industrial machine. Inembodiments, the mobile data collector is a mobile robot. Inembodiments, the mobile data collector is a mobile vehicle. Inembodiments, the mobile data collector is a handheld device. Inembodiments, the mobile data collector is a wearable device. Inembodiments, the severity corresponds to a severity unit. Inembodiments, the segment of a multi-segment vibration frequency spectrathat bounds the vibrations is determined by mapping the vibrations toone of a number of severity units based on the determined segment. Inembodiments, each of the severity units corresponds to a different rangeof the multi-segment vibration frequency spectra. In embodiments, themethod further comprises: mapping the vibrations to a first severityunit when the frequency of the vibrations corresponds to a below alow-end knee threshold-range of the multi-segment vibration frequencyspectra; mapping the vibrations to a second severity unit when thefrequency of the vibrations corresponds to a mid-range of themulti-segment vibration frequency spectra; and mapping the vibrations toa third severity unit when the frequency of the vibrations correspondsto an above the high-end knee threshold-range of the multi-segmentvibration frequency spectra. In embodiments, the ledger uses ablockchain structure to track transaction records for predictedmaintenance actions for the industrial machine. In embodiments, each ofthe transaction records is stored as a block in the blockchainstructure.

In embodiments, a method comprises: detecting an operatingcharacteristic of an industrial machine using one or more sensors of amobile data collector, the operating characteristic of the industrialmachine relating to vibrations detected for at least a portion of theindustrial machine; determining a severity of the operatingcharacteristic, the severity representing an impact of the operatingcharacteristic on the industrial machine, based on a segment of amulti-segment vibration frequency spectra that bounds the vibrations. Inembodiments, the severity corresponds to a severity unit. Inembodiments, the segment of a multi-segment vibration frequency spectrathat bounds the vibrations is determined by mapping the vibrations toone of a number of severity units based on the determined segment. Inembodiments, each of the severity units corresponds to a different rangeof the multi-segment vibration frequency spectra; predicting amaintenance action to perform against the industrial machine based onthe severity of the operating characteristic; and storing a transactionrecord of the predicted maintenance action within a ledger of serviceactivity associated with the industrial machine. In embodiments, theledger uses a blockchain structure to track transaction records forpredicted maintenance actions for the industrial machine. Inembodiments, each of the transaction records is stored as a block in theblockchain structure. In embodiments, the mobile data collector is amobile robot. In embodiments, the mobile data collector is a mobilevehicle. In embodiments, the mobile data collector is a handheld device.In embodiments, the mobile data collector is a wearable device. Inembodiments, determining the severity of the operating characteristiccomprises: mapping the vibrations to a first severity unit when thefrequency of the vibrations corresponds to a below a low-end kneethreshold-range of the multi-segment vibration frequency spectra;mapping the vibrations to a second severity unit when the frequency ofthe vibrations corresponds to a mid-range of the multi-segment vibrationfrequency spectra; and mapping the vibrations to a third severity unitwhen the frequency of the vibrations corresponds to an above thehigh-end knee threshold-range of the multi-segment vibration frequencyspectra.

In embodiments, a method comprises: deploying a mobile data collectorfor detecting and monitoring vibration activity of at least a portion ofan industrial machine, the mobile data collector including one or morevibration sensors; controlling the mobile data collector to approach alocation of the industrial machine within an industrial environment thatincludes the industrial machine; causing the one or more vibrationsensors of the mobile data collector to record one or more measurementsof the vibration activity; transmitting the one or more measurements ofthe vibration activity as vibration data to a server over a network;determining, at the server, a severity of the vibration activityrelative to timing by processing the vibration data; predicting, at theserver, a maintenance action to perform with respect to at least theportion of the industrial machine based on the severity of the vibrationactivity; and transmitting a signal indicative of the maintenance actionto the mobile data collector to cause the mobile data collector toperform the maintenance action. In embodiments, determining the severityof the vibration data relative to the timing by processing the vibrationdata comprises: determining a frequency of the vibration activity byprocessing the vibration data; determining, based on the frequency, asegment of a multi-segment vibration frequency spectra that bounds thevibration activity; and calculating a severity unit for the vibrationactivity based on the determined segment of the multi-segment vibrationfrequency spectra. In embodiments, calculating the severity unit for thevibration activity based on the determined segment of the multi-segmentvibration frequency spectra comprises: mapping the vibration activity tothe severity unit based on the determined segment of the multi-segmentvibration frequency spectra by: mapping the vibration activity to afirst severity unit when the frequency of the vibration activitycorresponds to a below a low-end knee threshold-range of themulti-segment vibration frequency spectra; mapping the vibrationactivity to a second severity unit when the frequency of the vibrationactivity corresponds to a mid-range of the multi-segment vibrationfrequency spectra; and mapping the vibration activity to a thirdseverity unit when the frequency of the vibration activity correspondsto an above the high-end knee threshold-range of the multi-segmentvibration frequency spectra. In embodiments, predicting the one or moremaintenance actions to perform with respect to at least the portion ofthe industrial machine based on the severity of the vibration activitycomprises: using intelligent systems associated with the server toprocess the vibration data against pre-recorded data for the industrialmachine. In embodiments, processing the vibration data against thepre-recorded data for the industrial machine includes identifying thepre-recorded data for the industrial machine within a knowledge baseassociated with the industrial environment; identifying an operatingcharacteristic of at least the portion of the machine based on thepre-recorded data for the industrial machine within the knowledge base;and predicting the one or more maintenance actions based on theoperating characteristic. In embodiments, the vibration activity isindicative of a waveform derived from a vibration envelope associatedwith the industrial machine. In embodiments, the one or more vibrationsensors detect the vibration activity when the mobile data collector isin near proximity to the industrial machine. In embodiments, thevibration activity represents velocity information for at least theportion of the industrial machine. In embodiments, the vibrationactivity represents frequency information for at least the portion ofthe industrial machine. In embodiments, the mobile data collector is oneof a plurality of mobile data collectors of a mobile data collectorswarm. In embodiments, the method further comprises usingself-organization systems of the mobile data collector swarm to controlmovements of the mobile data collector within an industrial environmentthat includes the industrial machine. In embodiments, the one or morevibration sensors detect the vibration activity when the mobile datacollector is in near proximity to the industrial machine. Inembodiments, using the self-organization systems of the mobile datacollector swarm to control the movements of the mobile data collectorwithin the industrial environment comprises controlling the movements ofthe mobile data collector within the industrial environment based onmovements of at least one other mobile data collector of the pluralityof mobile data collectors. In embodiments, the mobile data collector isa mobile robot and at least one other mobile data collector of theplurality of mobile data collectors is a mobile vehicle.

In embodiments, a method comprises: deploying a mobile data collectorfor detecting and monitoring vibration activity of at least a portion ofan industrial machine, the mobile data collector including one or morevibration sensors; controlling the mobile data collector to approach alocation of the industrial machine within an industrial environment thatincludes the industrial machine; causing the one or more vibrationsensors of the mobile data collector to record one or more measurementsof the vibration activity; transmitting the one or more measurements ofthe vibration activity as vibration data to a server over a network;determining, at the server, a frequency of the vibration activity byprocessing the vibration data; determining, at the server and based onthe frequency, a segment of a multi-segment vibration frequency spectrathat bounds the vibration activity; calculating, at the server, aseverity unit for the vibration activity based on the determined segmentof the multi-segment vibration frequency spectra; predicting, at theserver, a maintenance action to perform with respect to at least theportion of the industrial machine based on the severity unit; andtransmitting a signal indicative of the maintenance action to the mobiledata collector to cause the mobile data collector to perform themaintenance action. In embodiments, calculating the severity unit forthe vibration activity based on the determined segment of themulti-segment vibration frequency spectra comprises: mapping thevibration activity to the severity unit based on the determined segmentof the multi-segment vibration frequency spectra by: mapping thevibration activity to a first severity unit when the frequency of thevibration activity corresponds to a below a low-end knee threshold-rangeof the multi-segment vibration frequency spectra; mapping the vibrationactivity to a second severity unit when the frequency of the vibrationactivity corresponds to a mid-range of the multi-segment vibrationfrequency spectra; and mapping the vibration activity to a thirdseverity unit when the frequency of the vibration activity correspondsto an above the high-end knee threshold-range of the multi-segmentvibration frequency spectra. In embodiments, predicting the one or moremaintenance actions to perform with respect to at least the portion ofthe industrial machine based on the severity unit comprises: usingintelligent systems associated with the server to process the vibrationdata against pre-recorded data for the industrial machine. Inembodiments, processing the vibration data against the pre-recorded datafor the industrial machine includes identifying the pre-recorded datafor the industrial machine within a knowledge base associated with theindustrial environment; identifying an operating characteristic of atleast the portion of the machine based on the pre-recorded data for theindustrial machine within the knowledge base; and predicting the one ormore maintenance actions based on the operating characteristic. Inembodiments, the vibration activity is indicative of a waveform derivedfrom a vibration envelope associated with the industrial machine. Inembodiments, the one or more vibration sensors detect the vibrationactivity when the mobile data collector is in near proximity to theindustrial machine. In embodiments, the vibration activity representsvelocity information for at least the portion of the industrial machine.In embodiments, the vibration activity represents frequency informationfor at least the portion of the industrial machine. In embodiments, themobile data collector is one of a plurality of mobile data collectors ofa mobile data collector swarm. In embodiments, the method furthercomprises using self-organization systems of the mobile data collectorswarm to control movements of the mobile data collector within anindustrial environment that includes the industrial machine. Inembodiments, the one or more vibration sensors detect the vibrationactivity when the mobile data collector is in near proximity to theindustrial machine. In embodiments, using the self-organization systemsof the mobile data collector swarm to control the movements of themobile data collector within the industrial environment comprisescontrolling the movements of the mobile data collector within theindustrial environment based on movements of at least one other mobiledata collector of the plurality of mobile data collectors. Inembodiments, the mobile data collector is a mobile robot and at leastone other mobile data collector of the plurality of mobile datacollectors is a mobile vehicle.

In embodiments, a method comprises: deploying a mobile data collectorfor detecting and monitoring vibration activity of at least a portion ofan industrial machine, the mobile data collector including one or morevibration sensors; controlling the mobile data collector to approach alocation of the industrial machine within an industrial environment thatincludes the industrial machine; causing the one or more vibrationsensors of the mobile data collector to record one or more measurementsof the vibration activity; transmitting the one or more measurements ofthe vibration activity as vibration data to a server over a network;determining, at the server, a severity of the vibration activityrelative to timing by processing the vibration data; predicting, at theserver, a maintenance action to perform with respect to at least theportion of the industrial machine based on the severity of the vibrationactivity; transmitting a signal indicative of the maintenance action tothe mobile data collector to cause the mobile data collector to performthe maintenance action; and storing a record of the predictedmaintenance action within a ledger associated with the industrialmachine. In embodiments, determining the severity of the vibration datarelative to the timing by processing the vibration data comprises:determining a frequency of the vibration activity by processing thevibration data; determining, based on the frequency, a segment of amulti-segment vibration frequency spectra that bounds the vibrationactivity; and calculating a severity unit for the vibration activitybased on the determined segment of the multi-segment vibration frequencyspectra. In embodiments, calculating the severity unit for the vibrationactivity based on the determined segment of the multi-segment vibrationfrequency spectra comprises: mapping the vibration activity to theseverity unit based on the determined segment of the multi-segmentvibration frequency spectra by: mapping the vibration activity to afirst severity unit when the frequency of the vibration activitycorresponds to a below a low-end knee threshold-range of themulti-segment vibration frequency spectra; mapping the vibrationactivity to a second severity unit when the frequency of the vibrationactivity corresponds to a mid-range of the multi-segment vibrationfrequency spectra; and mapping the vibration activity to a thirdseverity unit when the frequency of the vibration activity correspondsto an above the high-end knee threshold-range of the multi-segmentvibration frequency spectra. In embodiments, predicting the one or moremaintenance actions to perform with respect to at least the portion ofthe industrial machine based on the severity of the vibration activitycomprises: using intelligent systems associated with the server toprocess the vibration data against pre-recorded data for the industrialmachine. In embodiments, processing the vibration data against thepre-recorded data for the industrial machine includes identifying thepre-recorded data for the industrial machine within a knowledge baseassociated with the industrial environment; identifying an operatingcharacteristic of at least the portion of the machine based on thepre-recorded data for the industrial machine within the knowledge base;and predicting the one or more maintenance actions based on theoperating characteristic. In embodiments, the vibration activity isindicative of a waveform derived from a vibration envelope associatedwith the industrial machine. In embodiments, the one or more vibrationsensors detect the vibration activity when the mobile data collector isin near proximity to the industrial machine. In embodiments, thevibration activity represents velocity information for at least theportion of the industrial machine. In embodiments, the vibrationactivity represents frequency information for at least the portion ofthe industrial machine. In embodiments, the mobile data collector is oneof a plurality of mobile data collectors of a mobile data collectorswarm. In embodiments, the method further comprises usingself-organization systems of the mobile data collector swarm to controlmovements of the mobile data collector within an industrial environmentthat includes the industrial machine. In embodiments, the one or morevibration sensors detect the vibration activity when the mobile datacollector is in near proximity to the industrial machine. Inembodiments, using the self-organization systems of the mobile datacollector swarm to control the movements of the mobile data collectorwithin the industrial environment comprises controlling the movements ofthe mobile data collector within the industrial environment based onmovements of at least one other mobile data collector of the pluralityof mobile data collectors. In embodiments, the mobile data collector isa mobile robot and at least one other mobile data collector of theplurality of mobile data collectors is a mobile vehicle. In embodiments,the ledger uses a blockchain structure to track transaction records forpredicted maintenance actions for the industrial machine. Inembodiments, each of the transaction records is stored as a block in theblockchain structure.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 through FIG. 5 are diagrammatic views that each depicts portionsof an overall view of an industrial IoT data collection, monitoring andcontrol system in accordance with the present disclosure.

FIG. 6 is a diagrammatic view of a platform including a local datacollection system disposed in an industrial environment for collectingdata from or about the elements of the environment, such as machines,components, systems, sub-systems, ambient conditions, states, workflows,processes, and other elements in accordance with the present disclosure.

FIG. 7 is a diagrammatic view that depicts elements of an industrialdata collection system for collecting analog sensor data in anindustrial environment in accordance with the present disclosure.

FIG. 8 is a diagrammatic view of a rotating or oscillating machinehaving a data acquisition module that is configured to collect waveformdata in accordance with the present disclosure.

FIG. 9 is a diagrammatic view of an exemplary tri-axial sensor mountedto a motor bearing of an exemplary rotating machine in accordance withthe present disclosure.

FIG. 10 and FIG. 11 are diagrammatic views of an exemplary tri-axialsensor and a single-axis sensor mounted to an exemplary rotating machinein accordance with the present disclosure.

FIG. 12 is a diagrammatic view of multiple machines under survey withensembles of sensors in accordance with the present disclosure.

FIG. 13 is a diagrammatic view of hybrid relational metadata and abinary storage approach in accordance with the present disclosure.

FIG. 14 is a diagrammatic view of components and interactions of a datacollection architecture involving application of cognitive and machinelearning systems to data collection and processing in accordance withthe present disclosure.

FIG. 15 is a diagrammatic view of components and interactions of a datacollection architecture involving application of a platform having acognitive data marketplace in accordance with the present disclosure.

FIG. 16 is a diagrammatic view of components and interactions of a datacollection architecture involving application of a self-organizing swarmof data collectors in accordance with the present disclosure.

FIG. 17 is a diagrammatic view of components and interactions of a datacollection architecture involving application of a haptic user interfacein accordance with the present disclosure.

FIG. 18 is a diagrammatic view of a multi-format streaming datacollection system in accordance with the present disclosure.

FIG. 19 is a diagrammatic view of combining legacy and streaming datacollection and storage in accordance with the present disclosure.

FIG. 20 is a diagrammatic view of industrial machine sensing using bothlegacy and updated streamed sensor data processing in accordance withthe present disclosure.

FIG. 21 is a diagrammatic view of an industrial machine sensed dataprocessing system that facilitates portal algorithm use and alignment oflegacy and streamed sensor data in accordance with the presentdisclosure.

FIG. 22 is a diagrammatic view of components and interactions of a datacollection architecture involving a streaming data acquisitioninstrument receiving analog sensor signals from an industrialenvironment connected to a cloud network facility in accordance with thepresent disclosure.

FIG. 23 is a diagrammatic view of components and interactions of a datacollection architecture involving a streaming data acquisitioninstrument having an alarms module, expert analysis module, and a driverAPI to facilitate communication with a cloud network facility inaccordance with the present disclosure.

FIG. 24 is a diagrammatic view of components and interactions of a datacollection architecture involving a streaming data acquisitioninstrument and first in, first out memory architecture to provide a realtime operating system in accordance with the present disclosure.

FIG. 25 through FIG. 30 are diagrammatic views of screens showing fouranalog sensor signals, transfer functions between the signals, analysisof each signal, and operating controls to move and edit throughout thestreaming signals obtained from the sensors in accordance with thepresent disclosure.

FIG. 31 is a diagrammatic view of components and interactions of a datacollection architecture involving a multiple streaming data acquisitioninstrument receiving analog sensor signals and digitizing those signalsto be obtained by a streaming hub server in accordance with the presentdisclosure.

FIG. 32 is a diagrammatic view of components and interactions of a datacollection architecture involving a master raw data server thatprocesses new streaming data and data already extracted and processed inaccordance with the present disclosure.

FIG. 33, FIG. 34, and FIG. 35 are diagrammatic views of components andinteractions of a data collection architecture involving a processing,analysis, report, and archiving server that processes new streaming dataand data already extracted and processed in accordance with the presentdisclosure.

FIG. 36 is a diagrammatic view of components and interactions of a datacollection architecture involving a relation database server and dataarchives and their connectivity with a cloud network facility inaccordance with the present disclosure.

FIG. 37 through FIG. 42 are diagrammatic views of components andinteractions of a data collection architecture involving a virtualstreaming data acquisition instrument receiving analog sensor signalsfrom an industrial environment connected to a cloud network facility inaccordance with the present disclosure.

FIG. 43 through FIG. 50 are diagrammatic views of components andinteractions of a data collection architecture involving data channelmethods and systems for data collection of industrial machines inaccordance with the present disclosure.

FIG. 51 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 52 and FIG. 53 are diagrammatic views that depict embodiments of adata monitoring device in accordance with the present disclosure.

FIG. 54 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 55 and 56 are diagrammatic views that depict an embodiment of asystem for data collection in accordance with the present disclosure.

FIGS. 57 and 58 are diagrammatic views that depict an embodiment of asystem for data collection comprising a plurality of data monitoringdevices in accordance with the present disclosure.

FIG. 59 depicts an embodiment of a data monitoring device incorporatingsensors in accordance with the present disclosure.

FIGS. 60 and 61 are diagrammatic views that depict embodiments of a datamonitoring device in communication with external sensors in accordancewith the present disclosure.

FIG. 62 is a diagrammatic view that depicts embodiments of a datamonitoring device with additional detail in the signal evaluationcircuit in accordance with the present disclosure.

FIG. 63 is a diagrammatic view that depicts embodiments of a datamonitoring device with additional detail in the signal evaluationcircuit in accordance with the present disclosure.

FIG. 64 is a diagrammatic view that depicts embodiments of a datamonitoring device with additional detail in the signal evaluationcircuit in accordance with the present disclosure.

FIG. 65 is a diagrammatic view that depicts embodiments of a system fordata collection in accordance with the present disclosure.

FIG. 66 is a diagrammatic view that depicts embodiments of a system fordata collection comprising a plurality of data monitoring devices inaccordance with the present disclosure.

FIG. 67 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 68 and 69 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 70 and 71 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 72 and 73 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 74 and 75 is a diagrammatic view that depicts embodiments of asystem for data collection comprising a plurality of data monitoringdevices in accordance with the present disclosure.

FIG. 76 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 77 and 78 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 79 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 80 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 81 and 82 are diagrammatic views that depict embodiments of asystem for data collection in accordance with the present disclosure.

FIGS. 83 and 84 are diagrammatic views that depict embodiments of asystem for data collection comprising a plurality of data monitoringdevices in accordance with the present disclosure.

FIG. 85 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 86 and 87 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 88 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 89 and 90 are diagrammatic views that depict embodiments of asystem for data collection in accordance with the present disclosure.

FIGS. 91 and 92 are diagrammatic views that depict embodiments of asystem for data collection comprising a plurality of data monitoringdevices in accordance with the present disclosure.

FIG. 93 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 94 and 95 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 96 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 97 and 98 are diagrammatic views that depict embodiments of asystem for data collection in accordance with the present disclosure.

FIGS. 99 and 100 are diagrammatic views that depict embodiments of asystem for data collection comprising a plurality of data monitoringdevices in accordance with the present disclosure.

FIG. 101 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 102 and 103 are diagrammatic views that depict embodiments of adata monitoring device in accordance with the present disclosure.

FIG. 104 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 105 and 106 are diagrammatic views that depict embodiments of asystem for data collection in accordance with the present disclosure.

FIGS. 107 and 108 are diagrammatic views that depict embodiments of asystem for data collection comprising a plurality of data monitoringdevices in accordance with the present disclosure.

FIG. 109 to FIG. 136 are diagrammatic views of components andinteractions of a data collection architecture involving various neuralnetwork embodiments interacting with a streaming data acquisitioninstrument receiving analog sensor signals and an expert analysis modulein accordance with the present disclosure.

FIG. 137 through FIG. 139 are diagrammatic views of components andinteractions of a data collection architecture involving a collector ofroute templates and the routing of data collectors in an industrialenvironment in accordance with the present disclosure.

FIG. 140 is a diagrammatic view that depicts a monitoring system thatemploys data collection bands in accordance with the present disclosure.

FIG. 141 is a diagrammatic view that depicts a system that employsvibration and other noise in predicting states and outcomes inaccordance with the present disclosure.

FIG. 142 is a diagrammatic view that depicts a system for datacollection in an industrial environment in accordance with the presentdisclosure.

FIG. 143 is a diagrammatic view that depicts an apparatus for datacollection in an industrial environment in accordance with the presentdisclosure.

FIG. 144 is a schematic flow diagram of a procedure for data collectionin an industrial environment in accordance with the present disclosure.

FIG. 145 is a diagrammatic view that depicts a system for datacollection in an industrial environment in accordance with the presentdisclosure.

FIG. 146 is a diagrammatic view that depicts an apparatus for datacollection in an industrial environment in accordance with the presentdisclosure.

FIG. 147 is a schematic flow diagram of a procedure for data collectionin an industrial environment in accordance with the present disclosure.

FIG. 148 is a diagrammatic view that depicts industry-specific feedbackin an industrial environment in accordance with the present disclosure.

FIG. 149 is a diagrammatic view that depicts an exemplary user interfacefor smart band configuration of a system for data collection in anindustrial environment is depicted in accordance with the presentdisclosure.

FIG. 150 is a diagrammatic view that depicts a graphical approach 11300for back-calculation in accordance with the present disclosure.

FIG. 151 is a diagrammatic view that depicts a wearable haptic userinterface device for providing haptic stimuli to a user that isresponsive to data collected in an industrial environment by a systemadapted to collect data in the industrial environment in accordance withthe present disclosure.

FIG. 152 is a diagrammatic view that depicts an augmented realitydisplay of heat maps based on data collected in an industrialenvironment by a system adapted to collect data in the environment inaccordance with the present disclosure.

FIG. 153 is a diagrammatic view that depicts an augmented realitydisplay including real time data overlaying a view of an industrialenvironment in accordance with the present disclosure.

FIG. 154 is a diagrammatic view that depicts a user interface displayand components of a neural net in a graphical user interface inaccordance with the present disclosure.

FIG. 155 is a diagrammatic view of components and interactions of a datacollection architecture involving swarming data collectors and sensormesh protocol in an industrial environment in accordance with thepresent disclosure.

FIG. 156 through FIG. 159 are diagrammatic views mobile sensorsplatforms in an industrial environment in accordance with the presentdisclosure.

FIG. 160 is a diagrammatic view of components and interactions of a datacollection architecture involving two mobile sensor platforms inspectinga vehicle during assembly in an industrial environment in accordancewith the present disclosure.

FIG. 161 and FIG. 162 are diagrammatic views one of the mobile sensorplatforms in an industrial environment in accordance with the presentdisclosure.

FIG. 163 is a diagrammatic view of components and interactions of a datacollection architecture involving two mobile sensor platforms inspectinga turbine engine during assembly in an industrial environment inaccordance with the present disclosure.

FIG. 164 is a diagrammatic view that depicts data collection systemaccording to some aspects of the present disclosure.

FIG. 165 is a diagrammatic view that depicts a system forself-organized, network-sensitive data collection in an industrialenvironment in accordance with the present disclosure.

FIG. 166 is a diagrammatic view that depicts an apparatus forself-organized, network-sensitive data collection in an industrialenvironment in accordance with the present disclosure.

FIG. 167 is a diagrammatic view that depicts an apparatus forself-organized, network-sensitive data collection in an industrialenvironment in accordance with the present disclosure.

FIG. 168 is a diagrammatic view that depicts an apparatus forself-organized, network-sensitive data collection in an industrialenvironment in accordance with the present disclosure.

FIG. 169 and FIG. 170 are diagrammatic views that depict embodiments oftransmission conditions in accordance with the present disclosure.

FIG. 171 is a diagrammatic view that depicts embodiments of a sensordata transmission protocol in accordance with the present disclosure.

FIG. 172 and FIG. 173 are diagrammatic views that depict embodiments ofbenchmarking data in accordance with the present disclosure.

FIG. 174 is a diagrammatic view that depicts embodiments of a system fordata collection and storage in an industrial environment in accordancewith the present disclosure.

FIG. 175 is a diagrammatic view that depicts embodiments of an apparatusfor self-organizing storage for data collection for an industrial systemin accordance with the present disclosure.

FIG. 176 is a diagrammatic view that depicts embodiments of a storagetime definition in accordance with the present disclosure.

FIG. 177 is a diagrammatic view that depicts embodiments of a dataresolution description in accordance with the present disclosure.

FIG. 178 and FIG. 179 diagrammatic views of an apparatus forself-organizing network coding for data collection for an industrialsystem in accordance with the present disclosure.

FIG. 180 and FIG. 181 diagrammatic views of data marketplace interactingwith data collection in an industrial system in accordance with thepresent disclosure.

FIG. 182 is a diagrammatic view that depicts a smart heating system asan element in a network for in an industrial Internet of Thingsecosystem in accordance with the present disclosure.

FIG. 183 is a schematic of a data network including server and clientnodes coupled by intermediate networks.

FIG. 184 is a block diagram illustrating the modules that implementTCP-based communication between a client node and a server node.

FIG. 185 is a block diagram illustrating the modules that implementPacket Coding Transmission Communication Protocol (PC-TCP) basedcommunication between a client node and a server node.

FIG. 186 is a schematic diagram of a use of the PC-TCP basedcommunication between a server and a module device on a cellularnetwork.

FIG. 187 is a block diagram of 1 PC-TCP module that uses a conventionalUDP module.

FIG. 188 is a block diagram of a PC-TCP module that is partiallyintegrated into a client application and partially implemented using aconventional UDP module.

FIG. 189 is a block diagram or a PC-TCP module that is split with userspace and kernel space components.

FIG. 190 is a block diagram for a proxy architecture.

FIG. 191 is a block diagram of a PC-TCP based proxy architecture inwhich a proxy node communicates using both PC-TCP and conventional TCP.

FIG. 192 is a block diagram of a PC-TCP proxy-based architectureembodied using a gateway device.

FIG. 193 is a block diagram of an alternative proxy architectureembodied within a client node.

FIG. 194 is a block diagram of a second PC-TCP based proxy architecturein which a proxy node communicates using both PC-TCP and conventionalTCP.

FIG. 195 is a block diagram of a PC-TCP proxy-based architectureembodied using a wireless access device.

FIG. 196 is a block diagram of a PC-TCP proxy-based architectureembodied cellular network.

FIG. 197 is a block diagram of a PC-TCP proxy-based architectureembodied cable television-based data network.

FIG. 198 is a block diagram of an intermediate proxy that communicateswith a client node and with a server node using separate PC-TCPconnections.

FIG. 199 is a block diagram of a PC-TCP proxy-based architectureembodied in a network device.

FIG. 200 is a block diagram of an intermediate proxy that recodescommunication between a client node and with a server node.

FIGS. 201-202 are diagrams that illustrates delivery of common contentto multiple destinations.

FIGS. 203-213 are schematic diagrams of various embodiments of PC-TCPcommunication approaches.

FIG. 214 is a block diagram of PC-TCP communication approach thatincludes window and rate control modules.

FIG. 215 is a schematic of a data network.

FIGS. 216-219 are block diagrams illustrating an embodiment PC-TCPcommunication approach that is configured according to a number oftunable parameters.

FIG. 220 is a diagram showing a network communication system.

FIG. 221 is a schematic diagram illustrating use of stored communicationparameters.

FIG. 222 is a schematic diagram illustrating a first embodiment ormulti-path content delivery.

FIGS. 223-225 are schematic diagrams illustrating a second embodiment ofmulti-path content delivery.

FIG. 226 is a diagrammatic view depicting an integrated cooktop ofintelligent cooking system methods and systems in accordance with thepresent teachings.

FIG. 227 is a diagrammatic view depicting a single intelligent burner ofthe intelligent cooking system in accordance with the present teachings.

FIG. 228 is a partial exterior view depicting a solar-powered hydrogenproduction and storage station in accordance with the present teachings.

FIG. 229 is a diagrammatic view depicting a low-pressure storage systemin accordance with the present teachings.

FIG. 230 and FIG. 231 are cross-sectional views of a low-pressurestorage system.

FIG. 232 is a diagrammatic view depicting an electrolyzer in accordancewith the present teachings.

FIG. 233 is a diagrammatic view depicting features of a platform thatinteract with electronic devices and participants in a related ecosystemof suppliers, content providers, service providers, and regulators inaccordance with the present teachings.

FIG. 234 is a diagrammatic view depicting a smart home embodiment of theintelligent cooking system in accordance with the present teachings.

FIG. 235 is a diagrammatic view depicting a hydrogen production and usesystem in accordance with the present teachings.

FIG. 236 is a diagrammatic view depicting an electrolytic cell inaccordance with the present teachings.

FIG. 237 is a diagrammatic view depicting a hydrogen production systemintegrated into a cooking system in accordance with the presentteachings.

FIG. 238 is a diagrammatic view depicting auto switching connectivity inthe form of ad hoc Wi-Fi from the cooktop through nearby mobile devicesin a normal connectivity mode when Wi-Fi is available in accordance withthe present teachings.

FIG. 239 is a diagrammatic view depicting an auto switching connectivityin the form of ad hoc Wi Fi from the cooktop through nearby mobiledevices for ad hoc use of the local mobile devices for connectivity tothe cloud in accordance with the present teachings.

FIG. 240 is a perspective view depicting a three-element induction smartcooking system in accordance with the present teachings.

FIG. 241 is a perspective view depicting a single burner gas smartcooking system in accordance with the present teachings.

FIG. 242 is a perspective view depicting an electric hot plate smartcooking system in accordance with the present teachings.

FIG. 243 is a perspective view depicting a single induction heatingelement smart cooking system in accordance with the present teachings.

FIGS. 244-251 are views of visual interfaces depicting user interfacefeatures of a smart knob in accordance with the present teachings.

FIG. 252 is a perspective view depicting a smart knob deployed on asingle heating element cooking system in accordance with the presentteachings.

FIG. 253 is a partial perspective view depicting a smart knob deployedon a side of a kitchen appliance for a single heating element cookingsystem in accordance with the present teachings.

FIGS. 254-257 are perspective views depicting smart temperature probesof the smart cooking system in accordance with the present teachings.

FIGS. 258-263 are diagrammatic views depicting different docks forcompatibility with a range of smart phone and tablet devices inaccordance with the present teachings.

FIG. 264 and FIG. 266 are diagrammatic views depicting a burner designcontemplated for use with a smart cooking system in accordance with thepresent teachings.

FIG. 265 is a cross sectional view of a burner design contemplated foruse with a smart cooking system.

FIG. 267, FIG. 269, and FIG. 271 are diagrammatic views depicting aburner design contemplated for use with a smart cooking system inaccordance with another example of the present teachings.

FIG. 268 and FIG. 270 are cross-sectional views of a burner design.

FIGS. 272-274 are diagrammatic views depicting a burner designcontemplated for use with a smart cooking system in accordance with afurther example of the present teachings.

FIGS. 275-277 are diagrammatic views depicting a burner designcontemplated for use with a smart cooking system in accordance with yetanother example of the present teachings.

FIG. 278 and FIG. 280 are diagrammatic views depicting a burner designcontemplated for use with a smart cooking system in accordance with anadditional example of the present teachings.

FIG. 279 is a cross-sectional view of a burner design contemplated foruse with a smart cooking system.

FIG. 281 is a flowchart depicting a method associated with a smartkitchen including a smart cooktop and an exhaust fan that may beautomatically turned on as water in a pot may begin to boil inaccordance with the present teachings.

FIG. 282 is an embodiment method and system related to renewable energysources for hydrogen production, storage, distribution and use aredepicted in accordance with the present teachings in accordance with thepresent teachings.

FIG. 283 is an alternate embodiment method and system related torenewable energy sources in accordance with the present teachings.

FIG. 284 is an alternate embodiment method and system related torenewable energy sources in accordance with the present teachings.

FIG. 285 depicts environments and manufacturing uses of hydrogenproduction, storage, distribution and use systems.

FIGS. 286-289 are diagrammatic views that depict embodiments of a systemfor using one or more wearable devices for mobile data collection inaccordance with the present disclosure.

FIGS. 290-292 are diagrammatic views that depict embodiments of a systemfor using one or more mobile robots and/or mobile vehicles for mobiledata collection in accordance with the present disclosure.

FIGS. 293-296 are diagrammatic views that depict embodiments of a systemfor using one or more handheld devices for mobile data collection inaccordance with the present disclosure.

FIGS. 297-299 are diagrammatic views that depict embodiments of acomputer vision system in accordance with the present disclosure.

FIGS. 300-301 are diagrammatic views that depict embodiments of a deeplearning system for training a computer vision system in accordance withthe present disclosure.

FIG. 302 depicts a predictive maintenance eco system networkarchitecture.

FIG. 303 depicts finding service workers using machine learning for thepredictive maintenance eco-system of FIG. 302.

FIG. 304 depicts ordering parts and service in a predictive maintenanceeco-system.

FIG. 305 depicts deployment of smart RFID elements in an industrialmachine environment.

FIG. 306 depicts a generalized data structure for machine information ina smart RFID.

FIG. 307 depicts a block level diagram of the storage structure of asmart RFID.

FIG. 308 depicts an example of data stored in a smart RFID.

FIG. 309 depicts a flow diagram of a method for collecting informationfrom a machine.

FIG. 310 depicts a flow diagram of a method for collecting data from aproduction environment.

FIG. 311 depicts an on-line maintenance management system withinterfaces for data sources updating information in the on-linemaintenance management system data storage.

FIG. 312 depicts a distributed ledger for predictive maintenanceinformation with role-specific access thereof.

FIG. 313 depicts a process for capturing images of portions of anindustrial machine.

FIG. 314 depicts a process that uses machine learning on images torecognize a likely internal structure of an industrial machine.

FIG. 315 depicts a knowledge graph of the predictive maintenancegathering information.

FIG. 316 depicts an artificial intelligence system generating servicerecommendations and the like based on predictive maintenance analysis.

FIG. 317 depicts a predictive maintenance timeline superimposed on apreventive maintenance timeline.

FIG. 318 depicts a block diagram of potential sources of diagnosticinformation.

FIG. 319 depicts a diagram of a process for rating vendors.

FIG. 320 depicts a diagram of a process for rating procedures

FIG. 321 depicts a diagram of Blockchain applied to transactions of apredictive maintenance eco-system.

FIG. 322 depicts a transfer function that facilitates convertingvibration data into severity units.

FIG. 323 depicts a table that facilitates mapping vibration data toseverity units.

FIG. 324 depicts a composite frequency graph for conventional vibrationassessment and severity unit-based assessment.

FIG. 325 depicts a rendering of a portion of an industrial machine foruse in an electronic user interface for depicting and discoveringseverity units and related information about a rotating component of theindustrial machine.

FIG. 326 depicts a data table of rotating component design parametersfor use in predicting maintenance events.

FIG. 327 a flow chart of predicting maintenance of at least one of agear, motor and roller bearing based on severity unit and actuatorcount, such as count of teeth in a gear.

DETAILED DESCRIPTION

Detailed embodiments of the present disclosure are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely exemplary of the disclosure, which may be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present disclosure in virtually anyappropriately detailed structure.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate with existing data collection, processing, and storage systemswhile preserving access to existing format/frequency range/resolutioncompatible data. While the industrial machine sensor data streamingfacilities described herein may collect a greater volume of data (e.g.,longer duration of data collection) from sensors at a wider range offrequencies and with greater resolution than existing data collectionsystems, methods and systems may be employed to provide access to datafrom the stream of data that represents one or more ranges of frequencyand/or one or more lines of resolution that are purposely compatiblewith existing systems. Further, a portion of the streamed data may beidentified, extracted, stored, and/or forwarded to existing dataprocessing systems to facilitate operation of existing data processingsystems that substantively matches operation of existing data processingsystems using existing collection-based data. In this way, a newlydeployed system for sensing aspects of industrial machines, such asaspects of moving parts of industrial machines, may facilitate continueduse of existing sensed data processing facilities, algorithms, models,pattern recognizers, user interfaces, and the like.

Through identification of existing frequency ranges, formats, and/orresolution, such as by accessing a data structure that defines theseaspects of existing data, higher resolution streamed data may beconfigured to represent a specific frequency, frequency range, format,and/or resolution. This configured streamed data can be stored in a datastructure that is compatible with existing sensed data structures sothat existing processing systems and facilities can access and processthe data substantially as if it were the existing data. One approach toadapting streamed data for compatibility with existing sensed data mayinclude aligning the streamed data with existing data so that portionsof the streamed data that align with the existing data can be extracted,stored, and made available for processing with existing data processingmethods. Alternatively, data processing methods may be configured toprocess portions of the streamed data that correspond, such as throughalignment, to the existing data, with methods that implement functionssubstantially similar to the methods used to process existing data, suchas methods that process data that contain a particular frequency rangeor a particular resolution and the like.

Methods used to process existing data may be associated with certaincharacteristics of sensed data, such as certain frequency ranges,sources of data, and the like. As an example, methods for processingbearing sensing information for a moving part of an industrial machinemay be capable of processing data from bearing sensors that fall into aparticular frequency range. This method can thusly be at least partiallyidentifiable by these characteristics of the data being processed.Therefore, given a set of conditions, such as moving device beingsensed, industrial machine type, frequency of data being sensed, and thelike, a data processing system may select an appropriate method. Also,given such a set of conditions, an industrial machine data sensing andprocessing facility may configure elements, such as data filters,routers, processors, and the like, to handle data meeting theconditions.

FIGS. 1 through 5 depict portions of an overall view of an industrialIoT data collection, monitoring and control system 10. FIG. 2 depicts amobile ad hoc network (“MANET”) 20, which may form a secure, temporalnetwork connection 22 (sometimes connected and sometimes isolated), witha cloud 30 or other remote networking system, so that network functionsmay occur over the MANET 20 within the environment, without the need forexternal networks, but at other times information can be sent to andfrom a central location. This allows the industrial environment to usethe benefits of networking and control technologies, while alsoproviding security, such as preventing cyber-attacks. The MANET 20 mayuse cognitive radio technologies 40, including those that form up anequivalent to the IP protocol, such as router 42, MAC 44, and physicallayer technologies 46. In certain embodiments, the system depicted inFIGS. 1 through 5 provides network-sensitive or network-aware transportof data over the network to and from a data collection device or a heavyindustrial machine.

FIGS. 3-4 depict intelligent data collection technologies deployedlocally, at the edge of an IoT deployment, where heavy industrialmachines are located. This includes various sensors 52, IoT devices 54,data storage capabilities (e.g., data pools 60, or distributed ledger62) (including intelligent, self-organizing storage), sensor fusion(including self-organizing sensor fusion), and the like. Interfaces fordata collection, including multi-sensory interfaces, tablets,smartphones 58, and the like are shown. FIG. 3 also shows the data pools60 that may collect data published by machines or sensors that detectconditions of machines, such as for later consumption by local or remoteintelligence. A distributed ledger system 62 may distribute storageacross the local storage of various elements of the environment, or morebroadly throughout the system. FIG. 4 also shows on-device sensor fusion80, such as for storing on a device data from multiple analog sensors82, which may be analyzed locally or in the cloud, such as by machinelearning 84, including by training a machine based on initial modelscreated by humans that are augmented by providing feedback (such asbased on measures of success) when operating the methods and systemsdisclosed herein.

FIG. 1 depicts a server based portion of an industrial IoT system thatmay be deployed in the cloud or on an enterprise owner's or operator'spremises. The server portion includes network coding (includingself-organizing network coding and/or automated configuration) that mayconfigure a network coding model based on feedback measures, networkconditions, or the like, for highly efficient transport of large amountsof data across the network to and from data collection systems and thecloud. Network coding may provide a wide range of capabilities forintelligence, analytics, remote control, remote operation, remoteoptimization, various storage configurations and the like, as depictedin FIG. 1. The various storage configurations may include distributedledger storage for supporting transactional data or other elements ofthe system.

FIG. 5 depicts a programmatic data marketplace 70, which may be aself-organizing marketplace, such as for making available data that iscollected in industrial environments, such as from data collectors, datapools, distributed ledgers, and other elements disclosed herein.Additional detail on the various components and sub-components of FIGS.1 through 5 is provided throughout this disclosure.

With reference to FIG. 6, an embodiment of platform 100 may include alocal data collection system 102, which may be disposed in anenvironment 104, such as an industrial environment similar to that shownin FIG. 3, for collecting data from or about the elements of theenvironment, such as machines, components, systems, sub-systems, ambientconditions, states, workflows, processes, and other elements. Theplatform 100 may connect to or include portions of the industrial IoTdata collection, monitoring and control system 10 depicted in FIGS. 1-5.The platform 100 may include a network data transport system 108, suchas for transporting data to and from the local data collection system102 over a network 110, such as to a host processing system 112, such asone that is disposed in a cloud computing environment or on the premisesof an enterprise, or that consists of distributed components thatinteract with each other to process data collected by the local datacollection system 102. The host processing system 112, referred to forconvenience in some cases as the host system 112, may include varioussystems, components, methods, processes, facilities, and the like forenabling automated, or automation-assisted processing of the data, suchas for monitoring one or more environments 104 or networks 110 or forremotely controlling one or more elements in a local environment 104 orin the network 110. The platform 100 may include one or more localautonomous systems, such as for enabling autonomous behavior, such asreflecting artificial, or machine-based intelligence or such as enablingautomated action based on the applications of a set of rules or modelsupon input data from the local data collection system 102 or from one ormore input sources 116, which may comprise information feeds and inputsfrom a wide array of sources, including those in the local environment104, in the network 110, in the host system 112, or in one or moreexternal systems, databases, or the like. The platform 100 may includeone or more intelligent systems 118, which may be disposed in,integrated with, or acting as inputs to one or more components of theplatform 100. Details of these and other components of the platform 100are provided throughout this disclosure.

Intelligent systems 118 may include cognitive systems 120, such asenabling a degree of cognitive behavior as a result of the coordinationof processing elements, such as mesh, peer-to-peer, ring, serial, andother architectures, where one or more node elements is coordinated withother node elements to provide collective, coordinated behavior toassist in processing, communication, data collection, or the like. TheMANET 20 depicted in FIG. 2 may also use cognitive radio technologies,including those that form up an equivalent to the IP protocol, such asrouter 42, MAC 44, and physical layer technologies 46. In one example,the cognitive system technology stack can include examples disclosed inU.S. Pat. No. 8,060,017 to Schlicht et al., issued 15 Nov. 2011 andhereby incorporated by reference as if fully set forth herein.

Intelligent systems may include machine learning systems 122, such asfor learning on one or more data sets. The one or more data sets mayinclude information collected using local data collection systems 102 orother information from input sources 116, such as to recognize states,objects, events, patterns, conditions, or the like that may, in turn, beused for processing by the host system 112 as inputs to components ofthe platform 100 and portions of the industrial IoT data collection,monitoring and control system 10, or the like. Learning may behuman-supervised or fully-automated, such as using one or more inputsources 116 to provide a data set, along with information about the itemto be learned. Machine learning may use one or more models, rules,semantic understandings, workflows, or other structured orsemi-structured understanding of the world, such as for automatedoptimization of control of a system or process based on feedback or feedforward to an operating model for the system or process. One suchmachine learning technique for semantic and contextual understandings,workflows, or other structured or semi-structured understandings isdisclosed in U.S. Pat. No. 8,200,775 to Moore, issued 12 Jun. 2012, andhereby incorporated by reference as if fully set forth herein. Machinelearning may be used to improve the foregoing, such as by adjusting oneor more weights, structures, rules, or the like (such as changing afunction within a model) based on feedback (such as regarding thesuccess of a model in a given situation) or based on iteration (such asin a recursive process). Where sufficient understanding of theunderlying structure or behavior of a system is not known, insufficientdata is not available, or in other cases where preferred for variousreasons, machine learning may also be undertaken in the absence of anunderlying model; that is, input sources may be weighted, structured, orthe like within a machine learning facility without regard to any apriori understanding of structure, and outcomes (such as those based onmeasures of success at accomplishing various desired objectives) can beserially fed to the machine learning system to allow it to learn how toachieve the targeted objectives. For example, the system may learn torecognize faults, to recognize patterns, to develop models or functions,to develop rules, to optimize performance, to minimize failure rates, tooptimize profits, to optimize resource utilization, to optimize flow(such as flow of traffic), or to optimize many other parameters that maybe relevant to successful outcomes (such as outcomes in a wide range ofenvironments). Machine learning may use genetic programming techniques,such as promoting or demoting one or more input sources, structures,data types, objects, weights, nodes, links, or other factors based onfeedback (such that successful elements emerge over a series ofgenerations). For example, alternative available sensor inputs for thedata collection system 102 may be arranged in alternative configurationsand permutations, such that the system may, using generic programmingtechniques over a series of data collection events, determine whatpermutations provide successful outcomes based on various conditions(such as conditions of components of the platform 100, conditions of thenetwork 110, conditions of the data collection system 102, conditions ofan environment 104), or the like. In embodiments, local machine learningmay turn on or off one or more sensors in a multi-sensor data collector102 in permutations over time, while tracking success outcomes such ascontributing to success in predicting a failure, contributing to aperformance indicator (such as efficiency, effectiveness, return oninvestment, yield, or the like), contributing to optimization of one ormore parameters, identification of a pattern (such as relating to athreat, a failure mode, a success mode, or the like) or the like. Forexample, a system may learn what sets of sensors should be turned on oroff under given conditions to achieve the highest value utilization of adata collector 102. In embodiments, similar techniques may be used tohandle optimization of transport of data in the platform 100 (such as inthe network 110) by using generic programming or other machine learningtechniques to learn to configure network elements (such as configuringnetwork transport paths, configuring network coding types andarchitectures, configuring network security elements), and the like.

In embodiments, the local data collection system 102 may include ahigh-performance, multi-sensor data collector having a number of novelfeatures for collection and processing of analog and other sensor data.In embodiments, the local data collection system 102 may be deployed tothe industrial facilities depicted in FIG. 3. A local data collectionsystem 102 may also be deployed to monitor other machines such as amachine 2300 in FIG. 9 and FIG. 10, the machines 2400, 2600, 2800, 2950,3000 depicted in FIG. 12, and the machines 3202, 3204 depicted in FIG.13. The data collection system 102 may have on-board intelligent systems118 (such as for learning to optimize the configuration and operation ofthe data collector, such as configuring permutations and combinations ofsensors based on contexts and conditions). In one example, the datacollection system 102 includes a crosspoint switch 130 or other analogswitches. Automated, intelligent configuration of the local datacollection system 102 may be based on a variety of types of information,such as information from various input sources, including those based onavailable power, power requirements of sensors, the value of the datacollected (such as based on feedback information from other elements ofthe platform 100), the relative value of information (such as valuesbased on the availability of other sources of the same or similarinformation), power availability (such as for powering sensors), networkconditions, ambient conditions, operating states, operating contexts,operating events, and many others.

FIG. 7 shows elements and sub-components of a data collection andanalysis system 1100 for sensor data (such as analog sensor data)collected in industrial environments. As depicted in FIG. 7, embodimentsof the methods and systems disclosed herein may include hardware thathas several different modules starting with the multiplexer (“MUX”) mainboard 1104. In embodiments, there may be a MUX option board 1108. TheMUX 114 main board is where the sensors connect to the system. Theseconnections are on top to enable ease of installation. Then there arenumerous settings on the underside of this board as well as on the Muxoption board 1108, which attaches to the MUX main board 1104 via twoheaders one at either end of the board. In embodiments, the Mux optionboard has the male headers, which mesh together with the female headeron the main Mux board. This enables them to be stacked on top of eachother taking up less real estate.

In embodiments, the main Mux board and/or the MUX option board thenconnects to the mother (e.g., with 4 simultaneous channels) and daughter(e.g., with 4 additional channels for 8 total channels) analog boards1110 via cables where some of the signal conditioning (such as hardwareintegration) occurs. The signals then move from the analog boards 1110to an anti-aliasing board (not shown) where some of the potentialaliasing is removed. The rest of the aliasing removal is done on thedelta sigma board 1112. The delta sigma board 1112 provides morealiasing protection along with other conditioning and digitizing of thesignal. Next, the data moves to the Jennic™ board 1114 for moredigitizing as well as communication to a computer via USB or Ethernet.In embodiments, the Jennic™ board 1114 may be replaced with a pic board1118 for more advanced and efficient data collection as well ascommunication. Once the data moves to the computer software 1102, thecomputer software 1102 can manipulate the data to show trending,spectra, waveform, statistics, and analytics.

In embodiments, the system is meant to take in all types of data fromvolts to 4-20 mA signals. In embodiments, open formats of data storageand communication may be used. In some instances, certain portions ofthe system may be proprietary especially some of research and dataassociated with the analytics and reporting. In embodiments, smart bandanalysis is a way to break data down into easily analyzed parts that canbe combined with other smart bands to make new more simplified yetsophisticated analytics. In embodiments, this unique information istaken and graphics are used to depict the conditions because picturedepictions are more helpful to the user. In embodiments, complicatedprograms and user interfaces are simplified so that any user canmanipulate the data like an expert.

In embodiments, the system in essence, works in a big loop. The systemstarts in software with a general user interface (“GUI”) 1124. Inembodiments, rapid route creation may take advantage of hierarchicaltemplates. In embodiments, a GUI is created so any general user canpopulate the information itself with simple templates. Once thetemplates are created the user can copy and paste whatever the userneeds. In addition, users can develop their own templates for futureease of use and to institutionalize the knowledge. When the user hasentered all of the user's information and connected all of the user'ssensors, the user can then start the system acquiring data.

Embodiments of the methods and systems disclosed herein may includeunique electrostatic protection for trigger and vibration inputs. Inmany critical industrial environments where large electrostatic forces,which can harm electrical equipment, may build up, for example rotatingmachinery or low-speed balancing using large belts, proper transducerand trigger input protection is required. In embodiments, a low-cost butefficient method is described for such protection without the need forexternal supplemental devices.

Typically, vibration data collectors are not designed to handle largeinput voltages due to the expense and the fact that, more often thannot, it is not needed. A need exists for these data collectors toacquire many varied types of RPM data as technology improves andmonitoring costs plummet. In embodiments, a method is using the alreadyestablished OptoMOS™ technology which permits the switching up front ofhigh voltage signals rather than using more conventional reed-relayapproaches. Many historic concerns regarding non-linear zero crossing orother non-linear solid-state behaviors have been eliminated with regardto the passing through of weakly buffered analog signals. In addition,in embodiments, printed circuit board routing topologies place all ofthe individual channel input circuitry as close to the input connectoras possible. In embodiments, a unique electrostatic protection fortrigger and vibration inputs may be placed upfront on the Mux and DAQhardware in order to dissipate the built up electric charge as thesignal passed from the sensor to the hardware. In embodiments, the Muxand analog board may support high-amperage input using a design topologycomprising wider traces and solid state relays for upfront circuitry.

In some systems multiplexers are afterthoughts and the quality of thesignal coming from the multiplexer is not considered. As a result of apoor quality multiplexer, the quality of the signal can drop as much as30 dB or more. Thus, substantial signal quality may be lost using a24-bit DAQ that has a signal to noise ratio of 110 dB and if the signalto noise ratio drops to 80 dB in the Mux, it may not be much better thana 16-bit system from 20 years ago. In embodiments of this system, animportant part at the front of the Mux is upfront signal conditioning onMux for improved signal-to-noise ratio. Embodiments may perform signalconditioning (such as range/gain control, integration, filtering, etc.)on vibration as well as other signal inputs up front before Muxswitching to achieve the highest signal-to-noise ratio.

In embodiments, in addition to providing a better signal, themultiplexer may provide a continuous monitor alarming feature. Trulycontinuous systems monitor every sensor all the time but tend to beexpensive. Typical multiplexer systems only monitor a set number ofchannels at one time and switch from bank to bank of a larger set ofsensors. As a result, the sensors not being currently collected are notbeing monitored; if a level increases the user may never know. Inembodiments, a multiplexer may have a continuous monitor alarmingfeature by placing circuitry on the multiplexer that can measure inputchannel levels against known alarm conditions even when the dataacquisition (“DAQ”) is not monitoring the input. In embodiments,continuous monitoring Mux bypass offers a mechanism whereby channels notbeing currently sampled by the Mux system may be continuously monitoredfor significant alarm conditions via a number of trigger conditionsusing filtered peak-hold circuits or functionally similar that are inturn passed on to the monitoring system in an expedient manner usinghardware interrupts or other means. This, in essence, makes the systemcontinuously monitoring, although without the ability to instantlycapture data on the problem like a true continuous system. Inembodiments, coupling this capability to alarm with adaptive schedulingtechniques for continuous monitoring and the continuous monitoringsystem's software adapting and adjusting the data collection sequencebased on statistics, analytics, data alarms and dynamic analysis mayallow the system to quickly collect dynamic spectral data on thealarming sensor very soon after the alarm sounds.

Another restriction of typical multiplexers is that they may have alimited number of channels. In embodiments, use of distributed complexprogrammable logic device (“CPLD”) chips with dedicated bus for logiccontrol of multiple Mux and data acquisition sections enables a CPLD tocontrol multiple mux and DAQs so that there is no limit to the number ofchannels a system can handle. Interfacing to multiple types ofpredictive maintenance and vibration transducers requires a great dealof switching. This includes AC/DC coupling, 4-20 interfacing, integratedelectronic piezoelectric transducer, channel power-down (for conservingop-amp power), single-ended or differential grounding options, and soon. Also required is the control of digital pots for range and gaincontrol, switches for hardware integration, AA filtering and triggering.This logic can be performed by a series of CPLD chips strategicallylocated for the tasks they control. A single giant CPLD requires longcircuit routes with a great deal of density at the single giant CPLD. Inembodiments, distributed CPLDs not only address these concerns but offera great deal of flexibility. A bus is created where each CPLD that has afixed assignment has its own unique device address. In embodiments,multiplexers and DAQs can stack together offering additional input andoutput channels to the system. For multiple boards (e.g., for multipleMux boards), jumpers are provided for setting multiple addresses. Inanother example, three bits permit up to 8 boards that are jumperconfigurable. In embodiments, a bus protocol is defined such that eachCPLD on the bus can either be addressed individually or as a group.

Typical multiplexers may be limited to collecting only sensors in thesame bank. For detailed analysis, this may be limiting as there istremendous value in being able to simultaneously review data fromsensors on the same machine. Current systems using conventional fixedbank multiplexers can only compare a limited number of channels (basedon the number of channels per bank) that were assigned to a particulargroup at the time of installation. The only way to provide someflexibility is to either overlap channels or incorporate lots ofredundancy in the system both of which can add considerable expense (insome cases an exponential increase in cost versus flexibility). Thesimplest Mux design selects one of many inputs and routes it into asingle output line. A banked design would consist of a group of thesesimple building blocks, each handling a fixed group of inputs androuting to its respective output. Typically, the inputs are notoverlapping so that the input of one Mux grouping cannot be routed intoanother. Unlike conventional Mux chips which typically switch a fixedgroup or banks of a fixed selection of channels into a single output(e.g., in groups of 2, 4, 8, etc.), a cross point Mux allows the user toassign any input to any output. Previously, crosspoint multiplexers wereused for specialized purposes such as RGB digital video applications andwere as a practical matter too noisy for analog applications such asvibration analysis; however more recent advances in the technology nowmake it feasible. Another advantage of the crosspoint Mux is the abilityto disable outputs by putting them into a high impedance state. This isideal for an output bus so that multiple Mux cards may be stacked, andtheir output buses joined together without the need for bus switches.

In embodiments, this may be addressed by use of an analog crosspointswitch for collecting variable groups of vibration input channels andproviding a matrix circuit, so the system may access any set of eightchannels from the total number of input sensors.

In embodiments, the ability to control multiple multiplexers with use ofdistributed CPLD chips with dedicated bus for logic control of multipleMux and data acquisition sections is enhanced with a hierarchicalmultiplexer which allows for multiple DAQ to collect data from multiplemultiplexers. A hierarchical Mux may allow modularly output of morechannels, such as 16, 24 or more to multiple of eight channel card sets.In embodiments, this allows for faster data collection as well as morechannels of simultaneous data collection for more complex analysis. Inembodiments, the Mux may be configured slightly to make it portable anduse data acquisition parking features, which turns SV3X DAQ into aprotected system embodiment.

In embodiments, once the signals leave the multiplexer and hierarchicalMux they move to the analog board where there are other enhancements. Inembodiments, power saving techniques may be used such as: power-down ofanalog channels when not in use; powering down of component boards;power-down of analog signal processing op-amps for non-selectedchannels; powering down channels on the mother and the daughter analogboards. The ability to power down component boards and other hardware bythe low-level firmware for the DAQ system makes high-level applicationcontrol with respect to power-saving capabilities relatively easy.Explicit control of the hardware is always possible but not required bydefault. In embodiments, this power saving benefit may be of value to aprotected system, especially if it is battery operated or solar powered.

In embodiments, in order to maximize the signal to noise ratio andprovide the best data, a peak-detector for auto-scaling routed into aseparate A/D will provide the system the highest peak in each set ofdata so it can rapidly scale the data to that peak. For vibrationanalysis purposes, the built-in A/D converters in many microprocessorsmay be inadequate with regards to number of bits, number of channels orsampling frequency versus not slowing the microprocessor downsignificantly. Despite these limitations, it is useful to use them forpurposes of auto-scaling. In embodiments, a separate A/D may be usedthat has reduced functionality and is cheaper. For each channel ofinput, after the signal is buffered (usually with the appropriatecoupling: AC or DC) but before it is signal conditioned, the signal isfed directly into the microprocessor or low-cost A/D.

Unlike the conditioned signal for which range, gain and filter switchesare thrown, no switches are varied. This permits the simultaneoussampling of the auto-scaling data while the input data is signalconditioned, fed into a more robust external A/D, and directed intoon-board memory using direct memory access (DMA) methods where memory isaccessed without requiring a CPU. This significantly simplifies theauto-scaling process by not having to throw switches and then allow forsettling time, which greatly slows down the auto-scaling process.Furthermore, the data may be collected simultaneously, which assures thebest signal-to-noise ratio. The reduced number of bits and otherfeatures is usually more than adequate for auto-scaling purposes. Inembodiments, improved integration using both analog and digital methodscreate an innovative hybrid integration which also improves or maintainsthe highest possible signal to noise ratio.

In embodiments, a section of the analog board may allow routing of atrigger channel, either raw or buffered, into other analog channels.This may allow a user to route the trigger to any of the channels foranalysis and trouble shooting. Systems may have trigger channels for thepurposes of determining relative phase between various input data setsor for acquiring significant data without the needless repetition ofunwanted input. In embodiments, digitally controlled relays may be usedto switch either the raw or buffered trigger signal into one of theinput channels. It may be desirable to examine the quality of thetriggering pulse because it may be corrupted for a variety of reasonsincluding inadequate placement of the trigger sensor, wiring issues,faulty setup issues such as a dirty piece of reflective tape if using anoptical sensor, and so on. The ability to look at either the raw orbuffered signal may offer an excellent diagnostic or debugging vehicle.It also can offer some improved phase analysis capability by making useof the recorded data signal for various signal processing techniquessuch as variable speed filtering algorithms.

In embodiments, once the signals leave the analog board, the signalsmove into the delta-sigma board where precise voltage reference for A/Dzero reference offers more accurate direct current sensor data. Thedelta sigma's high speeds also provide for using higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize antialiasing filter requirements. Lower oversampling rates canbe used for higher sampling rates. For example, a 3^(rd) order AA filterset for the lowest sampling requirement for 256 Hz (Fmax of 100 Hz) isthen adequate for Fmax ranges of 200 and 500 Hz. Another higher-cutoffAA filter can then be used for Fmax ranges from 1 kHz and higher (with asecondary filter kicking in at 2.56× the highest sampling rate of 128kHz). In embodiments, a CPLD may be used as a clock-divider for adelta-sigma A/D to achieve lower sampling rates without the need fordigital resampling. In embodiments, a high-frequency crystal referencecan be divided down to lower frequencies by employing a CPLD as aprogrammable clock divider. The accuracy of the divided down lowerfrequencies is even more accurate than the original source relative totheir longer time periods. This also minimizes or removes the need forresampling processing by the delta-sigma A/D.

In embodiments, the data then moves from the delta-sigma board to theJennic™ board where phase relative to input and trigger channels usingon-board timers may be digitally derived. In embodiments, the Jennic™board also has the ability to store calibration data and systemmaintenance repair history data in an on-board card set. In embodiments,the Jennic™ board will enable acquiring long blocks of data athigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates so it can stream data and acquire long blocksof data for advanced analysis in the future.

In embodiments, after the signal moves through the Jennic™ board it maythen be transmitted to the computer. In embodiments, the computersoftware will be used to add intelligence to the system starting with anexpert system GUI. The GUI may offer a graphical expert system withsimplified user interface for defining smart bands and diagnoses whichfacilitate anyone to develop complex analytics. In embodiments, thisuser interface may revolve around smart bands, which are a simplifiedapproach to complex yet flexible analytics for the general user. Inembodiments, the smart bands may pair with a self-learning neuralnetwork for an even more advanced analytical approach. In embodiments,this system may use the machine's hierarchy for additional analyticalinsight. One critical part of predictive maintenance is the ability tolearn from known information during repairs or inspections. Inembodiments, graphical approaches for back calculations may improve thesmart bands and correlations based on a known fault or problem.

In embodiments, there is a smart route which adapts which sensors itcollects simultaneously in order to gain additional correlativeintelligence. In embodiments, smart operational data store (“ODS”)allows the system to elect to gather data to perform operationaldeflection shape analysis in order to further examine the machinerycondition. In embodiments, adaptive scheduling techniques allow thesystem to change the scheduled data collected for full spectral analysisacross a number (e.g., eight), of correlative channels. In embodiments,the system may provide data to enable extended statistics capabilitiesfor continuous monitoring as well as ambient local vibration foranalysis that combines ambient temperature and local temperature andvibration level changes for identifying machinery issues.

In embodiments, a data acquisition device may be controlled by apersonal computer (PC) to implement the desired data acquisitioncommands. In embodiments, the DAQ box may be self-sufficient. and canacquire, process, analyze and monitor independent of external PCcontrol. Embodiments may include secure digital (SD) card storage. Inembodiments, significant additional storage capability may be providedby utilizing an SD card. This may prove critical for monitoringapplications where critical data may be stored permanently. Also, if apower failure should occur, the most recent data may be stored despitethe fact that it was not off-loaded to another system.

A current trend has been to make DAQ systems as communicative aspossible with the outside world usually in the form of networksincluding wireless. In the past it was common to use a dedicated bus tocontrol a DAQ system with either a microprocessor ormicrocontroller/microprocessor paired with a PC. In embodiments, a DAQsystem may comprise one or more microprocessor/microcontrollers,specialized microcontrollers/microprocessors, or dedicated processorsfocused primarily on the communication aspects with the outside world.These include USB, Ethernet and wireless with the ability to provide anIP address or addresses in order to host a webpage. All communicationswith the outside world are then accomplished using a simple text basedmenu. The usual array of commands (in practice more than a hundred) suchas InitializeCard, AcquireData, StopAcquisition, RetrieveCalibrationInfo, and so on, would be provided.

In embodiments, intense signal processing activities includingresampling, weighting, filtering, and spectrum processing may beperformed by dedicated processors such as field-programmable gate array(“FPGAs”), digital signal processor (“DSP”), microprocessors,microcontrollers, or a combination thereof. In embodiments, thissubsystem may communicate via a specialized hardware bus with thecommunication processing section. It will be facilitated with dual-portmemory, semaphore logic, and so on. This embodiment will not onlyprovide a marked improvement in efficiency but can significantly improvethe processing capability, including the streaming of the data as wellother high-end analytical techniques. This negates the need forconstantly interrupting the main processes which include the control ofthe signal conditioning circuits, triggering, raw data acquisition usingthe A/D, directing the A/D output to the appropriate on-board memory andprocessing that data.

Embodiments may include sensor overload identification. A need existsfor monitoring systems to identify when the sensor is overloading. Theremay be situations involving high-frequency inputs that will saturate astandard 100 mv/g sensor (which is most commonly used in the industry)and having the ability to sense the overload improves data quality forbetter analysis. A monitoring system may identify when their system isoverloading, but in embodiments, the system may look at the voltage ofthe sensor to determine if the overload is from the sensor, enabling theuser to get another sensor better suited to the situation, or gather thedata again.

Embodiments may include radio frequency identification (“RFID”) and aninclinometer or accelerometer on a sensor so the sensor can indicatewhat machine/bearing it is attached to and what direction such that thesoftware can automatically store the data without the user input. Inembodiments, users could put the system on any machine or machines andthe system would automatically set itself up and be ready for datacollection in seconds.

Embodiments may include ultrasonic online monitoring by placingultrasonic sensors inside transformers, motor control centers, breakersand the like and monitoring, via a sound spectrum, continuously lookingfor patterns that identify arcing, corona and other electrical issuesindicating a break down or issue. Embodiments may include providingcontinuous ultrasonic monitoring of rotating elements and bearings of anenergy production facility. In embodiments, an analysis engine may beused in ultrasonic online monitoring as well as identifying other faultsby combining the ultrasonic data with other parameters such asvibration, temperature, pressure, heat flux, magnetic fields, electricalfields, currents, voltage, capacitance, inductance, and combinations(e.g., simple ratios) of the same, among many others.

Embodiments of the methods and systems disclosed herein may include useof an analog crosspoint switch for collecting variable groups ofvibration input channels. For vibration analysis, it is useful to obtainmultiple channels simultaneously from vibration transducers mounted ondifferent parts of a machine (or machines) in multiple directions. Byobtaining the readings at the same time, for example, the relativephases of the inputs may be compared for the purpose of diagnosingvarious mechanical faults. Other types of cross channel analyses such ascross-correlation, transfer functions, Operating Deflection Shape(“ODS”) may also be performed.

Embodiments of the methods and systems disclosed herein may includeprecise voltage reference for A/D zero reference. Some A/D chips providetheir own internal zero voltage reference to be used as a mid-scalevalue for external signal conditioning circuitry to ensure that both theA/D and external op-amps use the same reference. Although this soundsreasonable in principle, there are practical complications. In manycases these references are inherently based on a supply voltage using aresistor-divider. For many current systems, especially those whose poweris derived from a PC via USB or similar bus, this provides for anunreliable reference, as the supply voltage will often vary quitesignificantly with load. This is especially true for delta-sigma A/Dchips which necessitate increased signal processing. Although theoffsets may drift together with load, a problem arises if one wants tocalibrate the readings digitally. It is typical to modify the voltageoffset expressed as counts coming from the A/D digitally to compensatefor the DC drift. However, for this case, if the proper calibrationoffset is determined for one set of loading conditions, they will notapply for other conditions. An absolute DC offset expressed in countswill no longer be applicable. As a result, it becomes necessary tocalibrate for all loading conditions which becomes complex, unreliable,and ultimately unmanageable. In embodiments, an external voltagereference is used which is simply independent of the supply voltage touse as the zero offset.

In embodiments, the system provides a phase-lock-loop band pass trackingfilter method for obtaining slow-speed RPMs and phase for balancingpurposes to remotely balance slow speed machinery, such as in papermills, as well as offering additional analysis from its data. Forbalancing purposes, it is sometimes necessary to balance at very slowspeeds. A typical tracking filter may be constructed based on aphase-lock loop or PLL design; however, stability and speed range areoverriding concerns. In embodiments, a number of digitally controlledswitches are used for selecting the appropriate RC and dampingconstants. The switching can be done all automatically after measuringthe frequency of the incoming tach signal. Embodiments of the methodsand systems disclosed herein may include digital derivation of phaserelative to input and trigger channels using on-board timers. Inembodiments, digital phase derivation uses digital timers to ascertainan exact delay from a trigger event to the precise start of dataacquisition. This delay, or offset, then, is further refined usinginterpolation methods to obtain an even more precise offset which isthen applied to the analytically determined phase of the acquired datasuch that the phase is “in essence” an absolute phase with precisemechanical meaning useful for among other things, one-shot balancing,alignment analysis, and so on.

Embodiments of the methods and systems disclosed herein may includesignal processing firmware/hardware. In embodiments, long blocks of datamay be acquired at high-sampling rate as opposed to multiple sets ofdata taken at different sampling rates. Typically, in modern routecollection for vibration analysis, it is customary to collect data at afixed sampling rate with a specified data length. The sampling rate anddata length may vary from route point to point based on the specificmechanical analysis requirements at hand. For example, a motor mayrequire a relatively low sampling rate with high resolution todistinguish running speed harmonics from line frequency harmonics. Thepractical trade-off here though is that it takes more collection time toachieve this improved resolution. In contrast, some high-speedcompressors or gear sets require much higher sampling rates to measurethe amplitudes of relatively higher frequency data although the preciseresolution may not be as necessary. Ideally, however, it would be betterto collect a very long sample length of data at a very high-samplingrate. When digital acquisition devices were first popularized in theearly 1980's, the A/D sampling, digital storage, and computationalabilities were not close to what they are today, so compromises weremade between the time required for data collection and the desiredresolution and accuracy. It was because of this limitation that someanalysts in the field even refused to give up their analog taperecording systems, which did not suffer as much from these samedigitizing drawbacks. A few hybrid systems were employed that woulddigitize the play back of the recorded analog data at multiple samplingrates and lengths desired, though these systems were admittedly lessautomated. The more common approach, as mentioned earlier, is to balancedata collection time with analysis capability and digitally acquire thedata blocks at multiple sampling rates and sampling lengths anddigitally store these blocks separately. In embodiments, a long datalength of data can be collected at the highest practical sampling rate(e.g., 102.4 kHz; corresponding to a 40 kHz Fmax) and stored. This longblock of data can be acquired in the same amount of time as the shorterlength of the lower sampling rates utilized by a priori methods so thatthere is no effective delay added to the sampling at the measurementpoint, always a concern in route collection. In embodiments, analog taperecording of data is digitally simulated with such a precision that itcan be in effect considered continuous or “analog” for many purposes,including for purposes of embodiments of the present disclosure, exceptwhere context indicates otherwise.

Embodiments of the methods and systems disclosed herein may includestorage of calibration data and maintenance history on-board card sets.Many data acquisition devices which rely on interfacing to a PC tofunction store their calibration coefficients on the PC. This isespecially true for complex data acquisition devices whose signal pathsare many and therefore whose calibration tables can be quite large. Inembodiments, calibration coefficients are stored in flash memory whichwill remember this data or any other significant information for thatmatter, for all practical purposes, permanently. This information mayinclude nameplate information such as serial numbers of individualcomponents, firmware or software version numbers, maintenance history,and the calibration tables. In embodiments, no matter which computer thebox is ultimately connected to, the DAQ box remains calibrated andcontinues to hold all of this critical information. The PC or externaldevice may poll for this information at any time for implantation orinformation exchange purposes.

Embodiments of the methods and systems disclosed herein may includerapid route creation taking advantage of hierarchical templates. In thefield of vibration monitoring, as well as parametric monitoring ingeneral, it is necessary to establish in a database or functionalequivalent the existence of data monitoring points. These points areassociated with a variety of attributes including the followingcategories: transducer attributes, data collection settings, machineryparameters and operating parameters. The transducer attributes wouldinclude probe type, probe mounting type and probe mounting direction oraxis orientation. Data collection attributes associated with themeasurement would involve a sampling rate, data length, integratedelectronic piezoelectric probe power and coupling requirements, hardwareintegration requirements, 4-20 or voltage interfacing, range and gainsettings (if applicable), filter requirements, and so on. Machineryparametric requirements relative to the specific point would includesuch items as operating speed, bearing type, bearing parametric datawhich for a rolling element bearing includes the pitch diameter, numberof balls, inner race, and outer-race diameters. For a tilting padbearing, this would include the number of pads and so on. Formeasurement points on a piece of equipment such as a gearbox, neededparameters would include, for example, the number of gear teeth on eachof the gears. For induction motors, it would include the number of rotorbars and poles; for compressors, the number of blades and/or vanes; forfans, the number of blades. For belt/pulley systems, the number of beltsas well as the relevant belt-passing frequencies may be calculated fromthe dimensions of the pulleys and pulley center-to-center distance. Formeasurements near couplings, the coupling type and number of teeth in ageared coupling may be necessary, and so on. Operating parametric datawould include operating load, which may be expressed in megawatts, flow(either air or fluid), percentage, horsepower, feet-per-minute, and soon. Operating temperatures both ambient and operational, pressures,humidity, and so on, may also be relevant. As can be seen, the setupinformation required for an individual measurement point can be quitelarge. It is also crucial to performing any legitimate analysis of thedata. Machinery, equipment, and bearing specific information areessential for identifying fault frequencies as well as anticipating thevarious kinds of specific faults to be expected. The transducerattributes as well as data collection parameters are vital for properlyinterpreting the data along with providing limits for the type ofanalytical techniques suitable. The traditional means of entering thisdata has been manual and quite tedious, usually at the lowesthierarchical level (for example, at the bearing level with regards tomachinery parameters), and at the transducer level for data collectionsetup information. It cannot be stressed enough, however, the importanceof the hierarchical relationships necessary to organize data—both foranalytical and interpretive purposes as well as the storage and movementof data. Here, we are focusing primarily on the storage and movement ofdata. By its nature, the aforementioned setup information is extremelyredundant at the level of the lowest hierarchies; however, because ofits strong hierarchical nature, it can be stored quite efficiently inthat form. In embodiments, hierarchical nature can be utilized whencopying data in the form of templates. As an example, hierarchicalstorage structure suitable for many purposes is defined from general tospecific of company, plant or site, unit or process, machine, equipment,shaft element, bearing, and transducer. It is much easier to copy dataassociated with a particular machine, piece of equipment, shaft elementor bearing than it is to copy only at the lowest transducer level. Inembodiments, the system not only stores data in this hierarchicalfashion, but robustly supports the rapid copying of data using thesehierarchical templates. Similarity of elements at specific hierarchicallevels lends itself to effective data storage in hierarchical format.For example, so many machines have common elements such as motors,gearboxes, compressors, belts, fans, and so on. More specifically, manymotors can be easily classified as induction, DC, fixed or variablespeed. Many gearboxes can be grouped into commonly occurring groupingssuch as input/output, input pinion/intermediate pinion/output pinion,4-posters, and so on. Within a plant or company, there are many similartypes of equipment purchased and standardized on for both cost andmaintenance reasons. This results in an enormous overlapping of similartypes of equipment and, as a result, offers a great opportunity fortaking advantage of a hierarchical template approach.

Embodiments of the methods and systems disclosed herein may includesmart bands. Smart bands refer to any processed signal characteristicsderived from any dynamic input or group of inputs for the purposes ofanalyzing the data and achieving the correct diagnoses. Furthermore,smart bands may even include mini or relatively simple diagnoses for thepurposes of achieving a more robust and complex one. Historically, inthe field of mechanical vibration analysis, Alarm Bands have been usedto define spectral frequency bands of interest for the purposes ofanalyzing and/or trending significant vibration patterns. The Alarm Bandtypically consists of a spectral (amplitude plotted against frequency)region defined between a low and high frequency border. The amplitudebetween these borders is summed in the same manner for which an overallamplitude is calculated. A Smart Band is more flexible in that it notonly refers to a specific frequency band but can also refer to a groupof spectral peaks such as the harmonics of a single peak, a true-peaklevel or crest factor derived from a time waveform, an overall derivedfrom a vibration envelope spectrum or other specialized signal analysistechnique or a logical combination (AND, OR, XOR, etc.) of these signalattributes. In addition, a myriad assortment of other parametric data,including system load, motor voltage and phase information, bearingtemperature, flow rates, and the like, can likewise be used as the basisfor forming additional smart bands. In embodiments, Smart Band symptomsmay be used as building blocks for an expert system whose engine wouldutilize these inputs to derive diagnoses. Some of these mini-diagnosesmay then in turn be used as Smart-Band symptoms (smart bands can includeeven diagnoses) for more generalized diagnoses.

Embodiments of the methods and systems disclosed herein may include aneural net expert system using smart bands. Typical vibration analysisengines are rule-based (i.e., they use a list of expert rules which,when met, trigger specific diagnoses). In contrast, a neural approachutilizes the weighted triggering of multiple input stimuli into smalleranalytical engines or neurons which in turn feed a simplified weightedoutput to other neurons. The output of these neurons can be alsoclassified as smart bands which in turn feed other neurons. Thisproduces a more layered approach to expert diagnosing as opposed to theone-shot approach of a rule-based system. In embodiments, the expertsystem utilizes this neural approach using smart bands; however, it doesnot preclude rule-based diagnoses being reclassified as smart bands asfurther stimuli to be utilized by the expert system. From thispoint-of-view, it can be overviewed as a hybrid approach, although atthe highest level it is essentially neural.

Embodiments of the methods and systems disclosed herein may include useof database hierarchy in analysis smart band symptoms and diagnoses maybe assigned to various hierarchical database levels. For example, asmart band may be called “Looseness” at the bearing level, trigger“Looseness” at the equipment level, and trigger “Looseness” at themachine level. Another example would be having a smart band diagnosiscalled “Horizontal Plane Phase Flip” across a coupling and generate asmart band diagnosis of “Vertical Coupling Misalignment” at the machinelevel.

Embodiments of the methods and systems disclosed herein may includeexpert system GUIs. In embodiments, the system undertakes a graphicalapproach to defining smart bands and diagnoses for the expert system.The entry of symptoms, rules, or more generally smart bands for creatinga particular machine diagnosis, may be tedious and time consuming. Onemeans of making the process more expedient and efficient is to provide agraphical means by use of wiring. The proposed graphical interfaceconsists of four major components: a symptom parts bin, diagnoses bin,tools bin, and graphical wiring area (“GWA”). In embodiments, a symptomparts bin includes various spectral, waveform, envelope and any type ofsignal processing characteristic or grouping of characteristics such asa spectral peak, spectral harmonic, waveform true-peak, waveformcrest-factor, spectral alarm band, and so on. Each part may be assignedadditional properties. For example, a spectral peak part may be assigneda frequency or order (multiple) of running speed. Some parts may bepre-defined or user defined such as a 1×, 2×, 3× running speed, 1×, 2×,3× gear mesh, 1×, 2×, 3× blade pass, number of motor rotor bars×runningspeed, and so on.

In embodiments, the diagnoses bin includes various pre-defined as wellas user-defined diagnoses such as misalignment, imbalance, looseness,bearing faults, and so on. Like parts, diagnoses may also be used asparts for the purposes of building more complex diagnoses. Inembodiments, the tools bin includes logical operations such as AND, OR,XOR, etc. or other ways of combining the various parts listed above suchas Find Max, Find Min, Interpolate, Average, other StatisticalOperations, etc. In embodiments, a graphical wiring area includes partsfrom the parts bin or diagnoses from the diagnoses bin and may becombined using tools to create diagnoses. The various parts, tools anddiagnoses will be represented with icons which are simply graphicallywired together in the desired manner.

Embodiments of the methods and systems disclosed herein may include agraphical approach for back-calculation definition. In embodiments, theexpert system also provides the opportunity for the system to learn. Ifone already knows that a unique set of stimuli or smart bandscorresponds to a specific fault or diagnosis, then it is possible toback-calculate a set of coefficients that when applied to a future setof similar stimuli would arrive at the same diagnosis. In embodiments,if there are multiple sets of data, a best-fit approach may be used.Unlike the smart band GUI, this embodiment will self-generate a wiringdiagram. In embodiments, the user may tailor the back-propagationapproach settings and use a database browser to match specific sets ofdata with the desired diagnoses. In embodiments, the desired diagnosesmay be created or custom tailored with a smart band GUI. In embodiments,after that, a user may press the GENERATE button and a dynamic wiring ofthe symptom-to-diagnosis may appear on the screen as it works throughthe algorithms to achieve the best fit. In embodiments, when complete, avariety of statistics are presented which detail how well the mappingprocess proceeded. In some cases, no mapping may be achieved if, forexample, the input data was all zero or the wrong data (mistakenlyassigned) and so on. Embodiments of the methods and systems disclosedherein may include bearing analysis methods. In embodiments, bearinganalysis methods may be used in conjunction with a computer aided design(“CAD”), predictive deconvolution, minimum variance distortionlessresponse (“MVDR”) and spectrum sum-of-harmonics.

In recent years, there has been a strong drive to save power which hasresulted in an influx of variable frequency drives and variable speedmachinery. In embodiments, a bearing analysis method is provided. Inembodiments, torsional vibration detection and analysis is providedutilizing transitory signal analysis to provide an advanced torsionalvibration analysis for a more comprehensive way to diagnose machinerywhere torsional forces are relevant (such as machinery with rotatingcomponents). Due primarily to the decrease in cost of motor speedcontrol systems, as well as the increased cost and consciousness ofenergy-usage, it has become more economically justifiable to takeadvantage of the potentially vast energy savings of load control.Unfortunately, one frequently overlooked design aspect of this issue isthat of vibration. When a machine is designed to run at only one speed,it is far easier to design the physical structure accordingly so as toavoid mechanical resonances both structural and torsional, each of whichcan dramatically shorten the mechanical health of a machine. This wouldinclude such structural characteristics as the types of materials touse, their weight, stiffening member requirements and placement, bearingtypes, bearing location, base support constraints, etc. Even withmachines running at one speed, designing a structure so as to minimizevibration can prove a daunting task, potentially requiring computermodeling, finite-element analysis, and field testing. By throwingvariable speeds into the mix, in many cases, it becomes impossible todesign for all desirable speeds. The problem then becomes one ofminimization, e.g., by speed avoidance. This is why many modern motorcontrollers are typically programmed to skip or quickly pass throughspecific speed ranges or bands. Embodiments may include identifyingspeed ranges in a vibration monitoring system. Non-torsional, structuralresonances are typically fairly easy to detect using conventionalvibration analysis techniques. However, this is not the case fortorsion. One special area of current interest is the increased incidenceof torsional resonance problems, apparently due to the increasedtorsional stresses of speed change as well as the operation of equipmentat torsional resonance speeds. Unlike non-torsional structuralresonances which generally manifest their effect with dramaticallyincreased casing or external vibration, torsional resonances generallyshow no such effect. In the case of a shaft torsional resonance, thetwisting motion induced by the resonance may only be discernible bylooking for speed and/or phase changes. The current standard methodologyfor analyzing torsional vibration involves the use of specializedinstrumentation. Methods and systems disclosed herein allow analysis oftorsional vibration without such specialized instrumentation. This mayconsist of shutting the machine down and employing the use of straingauges and/or other special fixturing such as speed encoder platesand/or gears. Friction wheels are another alternative, but theytypically require manual implementation and a specialized analyst. Ingeneral, these techniques can be prohibitively expensive and/orinconvenient. An increasing prevalence of continuous vibrationmonitoring systems due to decreasing costs and increasing convenience(e.g., remote access) exists. In embodiments, there is an ability todiscern torsional speed and/or phase variations with just the vibrationsignal. In embodiments, transient analysis techniques may be utilized todistinguish torsionally induced vibrations from mere speed changes dueto process control. In embodiments, factors for discernment might focuson one or more of the following aspects: the rate of speed change due tovariable speed motor control would be relatively slow, sustained anddeliberate; torsional speed changes would tend to be short, impulsiveand not sustained; torsional speed changes would tend to be oscillatory,most likely decaying exponentially, process speed changes would not; andsmaller speed changes associated with torsion relative to the shaft'srotational speed which suggest that monitoring phase behavior would showthe quick or transient speed bursts in contrast to the slow phasechanges historically associated with ramping a machine's speed up ordown (as typified with Bode or Nyquist plots).

Embodiments of the methods and systems disclosed herein may includeimproved integration using both analog and digital methods. When asignal is digitally integrated using software, essentially the spectrallow-end frequency data has its amplitude multiplied by a function whichquickly blows up as it approaches zero and creates what is known in theindustry as a “ski-slope” effect. The amplitude of the ski-slope isessentially the noise floor of the instrument. The simple remedy forthis is the traditional hardware integrator, which can perform atsignal-to-noise ratios much greater than that of an already digitizedsignal. It can also limit the amplification factor to a reasonable levelso that multiplication by very large numbers is essentially prohibited.However, at high frequencies where the frequency becomes large, theoriginal amplitude which may be well above the noise floor is multipliedby a very small number (1/f) that plunges it well below the noise floor.The hardware integrator has a fixed noise floor that although low floordoes not scale down with the now lower amplitude high-frequency data. Incontrast, the same digital multiplication of a digitized high-frequencysignal also scales down the noise floor proportionally. In embodiments,hardware integration may be used below the point of unity gain where (ata value usually determined by units and/or desired signal to noise ratiobased on gain) and software integration may be used above the value ofunity gain to produce an ideal result. In embodiments, this integrationis performed in the frequency domain. In embodiments, the resultinghybrid data can then be transformed back into a waveform which should befar superior in signal-to-noise ratio when compared to either hardwareintegrated or software integrated data. In embodiments, the strengths ofhardware integration are used in conjunction with those of digitalsoftware integration to achieve the maximum signal-to-noise ratio. Inembodiments, the first order gradual hardware integrator high passfilter along with curve fitting allow some relatively low frequency datato get through while reducing or eliminating the noise, allowing veryuseful analytical data that steep filters kill to be salvaged.

Embodiments of the methods and systems disclosed herein may includeadaptive scheduling techniques for continuous monitoring. Continuousmonitoring is often performed with an up-front Mux whose purpose it isto select a few channels of data among many to feed the hardware signalprocessing, A/D, and processing components of a DAQ system. This is doneprimarily out of practical cost considerations. The tradeoff is that allof the points are not monitored continuously (although they may bemonitored to a lesser extent via alternative hardware methods). Inembodiments, multiple scheduling levels are provided. In embodiments, atthe lowest level, which is continuous for the most part, all of themeasurement points will be cycled through in round-robin fashion. Forexample, if it takes 30 seconds to acquire and process a measurementpoint and there are 30 points, then each point is serviced once every 15minutes; however, if a point should alarm by whatever criteria the userselects, its priority level can be increased so that it is serviced moreoften. As there can be multiple grades of severity for each alarm, socan there me multiple levels of priority with regards to monitoring. Inembodiments, more severe alarms will be monitored more frequently. Inembodiments, a number of additional high-level signal processingtechniques can be applied at less frequent intervals. Embodiments maytake advantage of the increased processing power of a PC and the PC cantemporarily suspend the round-robin route collection (with its multipletiers of collection) process and stream the required amount of data fora point of its choosing. Embodiments may include various advancedprocessing techniques such as envelope processing, wavelet analysis, aswell as many other signal processing techniques. In embodiments, afteracquisition of this data, the DAQ card set will continue with its routeat the point it was interrupted. In embodiments, various PC scheduleddata acquisitions will follow their own schedules which will be lessfrequency than the DAQ card route. They may be set up hourly, daily, bynumber of route cycles (for example, once every 10 cycles) and alsoincreased scheduling-wise based on their alarm severity priority or typeof measurement (e.g., motors may be monitored differently than fans).

Embodiments of the methods and systems disclosed herein may include dataacquisition parking features. In embodiments, a data acquisition boxused for route collection, real time analysis and in general as anacquisition instrument can be detached from its PC (tablet or otherwise)and powered by an external power supply or suitable battery. Inembodiments, the data collector still retains continuous monitoringcapability and its on-board firmware can implement dedicated monitoringfunctions for an extended period of time or can be controlled remotelyfor further analysis. Embodiments of the methods and systems disclosedherein may include extended statistical capabilities for continuousmonitoring.

Embodiments of the methods and systems disclosed herein may includeambient sensing plus local sensing plus vibration for analysis. Inembodiments, ambient environmental temperature and pressure, sensedtemperature and pressure may be combined with long/medium term vibrationanalysis for prediction of any of a range of conditions orcharacteristics. Variants may add infrared sensing, infraredthermography, ultrasound, and many other types of sensors and inputtypes in combination with vibration or with each other. Embodiments ofthe methods and systems disclosed herein may include a smart route. Inembodiments, the continuous monitoring system's software willadapt/adjust the data collection sequence based on statistics,analytics, data alarms and dynamic analysis. Typically, the route is setbased on the channels the sensors are attached to. In embodiments, withthe crosspoint switch, the Mux can combine any input Mux channels to the(e.g., eight) output channels. In embodiments, as channels go into alarmor the system identifies key deviations, it will pause the normal routeset in the software to gather specific simultaneous data, from thechannels sharing key statistical changes, for more advanced analysis.Embodiments include conducting a smart ODS or smart transfer function.

Embodiments of the methods and systems disclosed herein may includesmart ODS and one or more transfer functions. In embodiments, due to asystem's multiplexer and crosspoint switch, an ODS, a transfer function,or other special tests on all the vibration sensors attached to amachine/structure can be performed and show exactly how the machine'spoints are moving in relationship to each other. In embodiments, 40-50kHz and longer data lengths (e.g., at least one minute) may be streamed,which may reveal different information than what a normal ODS ortransfer function will show. In embodiments, the system will be able todetermine, based on the data/statistics/analytics to use, the smartroute feature that breaks from the standard route and conducts an ODSacross a machine, structure or multiple machines and structures thatmight show a correlation because the conditions/data directs it. Inembodiments, for the transfer functions there may be an impact hammerused on one channel and then compared against other vibration sensors onthe machine. In embodiments, the system may use the condition changessuch as load, speed, temperature or other changes in the machine orsystem to conduct the transfer function. In embodiments, differenttransfer functions may be compared to each other over time. Inembodiments, difference transfer functions may be strung together like amovie that may show how the machinery fault changes, such as a bearingthat could show how it moves through the four stages of bearing failureand so on. Embodiments of the methods and systems disclosed herein mayinclude a hierarchical Mux.

With reference to FIG. 8, the present disclosure generally includesdigitally collecting or streaming waveform data 2010 from a machine 2020whose operational speed can vary from relatively slow rotational oroscillational speeds to much higher speeds in different situations. Thewaveform data 2010, at least on one machine, may include data from asingle axis sensor 2030 mounted at an unchanging reference location 2040and from a three-axis sensor 2050 mounted at changing locations (orlocated at multiple locations), including location 2052. In embodiments,the waveform data 2010 can be vibration data obtained simultaneouslyfrom each sensor 2030, 2050 in a gap-free format for a duration ofmultiple minutes with maximum resolvable frequencies sufficiently largeto capture periodic and transient impact events. By way of this example,the waveform data 2010 can include vibration data that can be used tocreate an operational deflecting shape. It can also be used, as needed,to diagnose vibrations from which a machine repair solution can beprescribed.

In embodiments, the machine 2020 can further include a housing 2100 thatcan contain a drive motor 2110 that can drive a shaft 2120. The shaft2120 can be supported for rotation or oscillation by a set of bearings2130, such as including a first bearing 2140 and a second bearing 2150.A data collection module 2160 can connect to (or be resident on) themachine 2020. In one example, the data collection module 2160 can belocated and accessible through a cloud network facility 2170, cancollect the waveform data 2010 from the machine 2020, and deliver thewaveform data 2010 to a remote location. A working end 2180 of the driveshaft 2120 of the machine 2020 can drive a windmill, a fan, a pump, adrill, a gear system, a drive system, or other working element, as thetechniques described herein can apply to a wide range of machines,equipment, tools, or the like that include rotating or oscillatingelements. In other instances, a generator can be substituted for themotor 2110, and the working end of the drive shaft 2120 can directrotational energy to the generator to generate power, rather thanconsume it.

In embodiments, the waveform data 2010 can be obtained using apredetermined route format based on the layout of the machine 2020. Thewaveform data 2010 may include data from the single axis sensor 2030 andthe three-axis sensor 2050. The single-axis sensor 2030 can serve as areference probe with its one channel of data and can be fixed at theunchanging location 2040 on the machine under survey. The three-axissensor 2050 can serve as a tri-axial probe (e.g., three orthogonal axes)with its three channels of data and can be moved along a predetermineddiagnostic route format from one test point to the next test point. Inone example, both sensors 2030, 2050 can be mounted manually to themachine 2020 and can connect to a separate portable computer in certainservice examples. The reference probe can remain at one location whilethe user can move the tri-axial vibration probe along the predeterminedroute, such as from bearing-to-bearing on a machine. In this example,the user is instructed to locate the sensors at the predeterminedlocations to complete the survey (or portion thereof) of the machine.

With reference to FIG. 9, a portion of an exemplary machine 2200 isshown having a tri-axial sensor 2210 mounted to a location 2220associated with a motor bearing of the machine 2200 with an output shaft2230 and output member 2240 in accordance with the present disclosure.With reference to FIG. 10, the exemplary machine 2300 is shown having atri-axial sensor 2310 and a single-axis vibration sensor 2320 serving asthe reference sensor that is attached on the machine 2300 at anunchanging location for the duration of the vibration survey inaccordance with the present disclosure. The tri-axial sensor 2310 andthe single-axis vibration sensor 2320 can be connected to a datacollection system 2330.

In further examples, the sensors and data acquisition modules andequipment can be integral to, or resident on, the rotating machine. Byway of these examples, the machine can contain many single axis sensorsand many tri-axial sensors at predetermined locations. The sensors canbe originally installed equipment and provided by the original equipmentmanufacturer or installed at a different time in a retrofit application.The data collection module 2160, or the like, can select and use onesingle axis sensor and obtain data from it exclusively during thecollection of waveform data 2010 while moving to each of the tri-axialsensors. The data collection module 2160 can be resident on the machine2020 and/or connect via the cloud network facility 2170.

With reference to FIG. 8, the various embodiments include collecting thewaveform data 2010 by digitally recording locally, or streaming over,the cloud network facility 2170. The waveform data 2010 can be collectedso as to be gap-free with no interruptions and, in some respects, can besimilar to an analog recording of waveform data. The waveform data 2010from all of the channels can be collected for one to two minutesdepending on the rotating or oscillating speed of the machine beingmonitored. In embodiments, the data sampling rate can be at a relativelyhigh-sampling rate relative to the operating frequency of the machine2020.

In embodiments, a second reference sensor can be used, and a fifthchannel of data can be collected. As such, the single-axis sensor can bethe first channel and tri-axial vibration can occupy the second, thethird, and the fourth data channels. This second reference sensor, likethe first, can be a single axis sensor, such as an accelerometer. Inembodiments, the second reference sensor, like the first referencesensor, can remain in the same location on the machine for the entirevibration survey on that machine. The location of the first referencesensor (i.e., the single axis sensor) may be different than the locationof the second reference sensors (i.e., another single axis sensor). Incertain examples, the second reference sensor can be used when themachine has two shafts with different operating speeds, with the tworeference sensors being located on the two different shafts. Inaccordance with this example, further single-axis reference sensors canbe employed at additional but different unchanging locations associatedwith the rotating machine.

In embodiments, the waveform data can be transmitted electronically in agap-free free format at a significantly high rate of sampling for arelatively longer period of time. In one example, the period of time is60 seconds to 120 seconds. In another example, the rate of sampling is100 kHz with a maximum resolvable frequency (Fmax) of 40 kHz. It will beappreciated in light of this disclosure that the waveform data can beshown to approximate more closely some of the wealth of data availablefrom previous instances of analog recording of waveform data.

In embodiments, sampling, band selection, and filtering techniques canpermit one or more portions of a long stream of data (i.e., one to twominutes in duration) to be under sampled or over sampled to realizevarying effective sampling rates. To this end, interpolation anddecimation can be used to further realize varying effective samplingrates. For example, oversampling may be applied to frequency bands thatare proximal to rotational or oscillational operating speeds of thesampled machine, or to harmonics thereof, as vibration effects may tendto be more pronounced at those frequencies across the operating range ofthe machine. In embodiments, the digitally-sampled data set can bedecimated to produce a lower sampling rate. It will be appreciated inlight of the disclosure that decimate in this context can be theopposite of interpolate. In embodiments, decimating the data set caninclude first applying a low-pass filter to the digitally-sampled dataset and then undersampling the data set.

In one example, a sample waveform at 100 Hz can be undersampled at everytenth point of the digital waveform to produce an effective samplingrate of 10 Hz, but the remaining nine points of that portion of thewaveform are effectively discarded and not included in the modeling ofthe sample waveform. Moreover, this type of bare undersampling cancreate ghost frequencies due to the undersampling rate (i.e., 10 Hz)relative to the 100 Hz sample waveform.

Most hardware for analog-to-digital conversions uses a sample-and-holdcircuit that can charge up a capacitor for a given amount of time suchthat an average value of the waveform is determined over a specificchange in time. It will be appreciated in light of the disclosure thatthe value of the waveform over the specific change in time is not linearbut more similar to a cardinal sinusoidal (“sine”) function; therefore,it can be shown that more emphasis can be placed on the waveform data atthe center of the sampling interval with exponential decay of thecardinal sinusoidal signal occurring from its center.

By way of the above example, the sample waveform at 100 Hz can behardware-sampled at 10 Hz and therefore each sampling point is averagedover 100 milliseconds (e.g., a signal sampled at 100 Hz can have eachpoint averaged over 10 milliseconds). In contrast to the effectivediscarding of nine out of the ten data points of the sampled waveform asdiscussed above, the present disclosure can include weighing adjacentdata. The adjacent data can refer to the sample points that werepreviously discarded and the one remaining point that was retained. Inone example, a low pass filter can average the adjacent sample datalinearly, i.e., determining the sum of every ten points and thendividing that sum by ten. In a further example, the adjacent data can beweighted with a sine function. The process of weighting the originalwaveform with the sine function can be referred to as an impulsefunction or can be referred to in the time domain as a convolution.

The present disclosure can be applicable to not only digitizing awaveform signal based on a detected voltage but can also be applicableto digitizing waveform signals based on current waveforms, vibrationwaveforms, and image processing signals including video signalrasterization. In one example, the resizing of a window on a computerscreen can be decimated, albeit in at least two directions. In thesefurther examples, it will be appreciated that undersampling by itselfcan be shown to be insufficient. To that end, oversampling or upsamplingby itself can similarly be shown to be insufficient, such thatinterpolation can be used like decimation but in lieu of onlyundersampling by itself.

It will be appreciated in light of the disclosure that interpolation inthis context can refer to first applying a low pass filter to thedigitally-sampled waveform data and then upsampling the waveform data.It will be appreciated in light of the disclosure that real-worldexamples can often require the use of use non-integer factors fordecimation or interpolation, or both. To that end, the presentdisclosure includes interpolating and decimating sequentially in orderto realize a non-integer factor rate for interpolating and decimating.In one example, interpolating and decimating sequentially can defineapplying a low-pass filter to the sample waveform, then interpolatingthe waveform after the low-pass filter, and then decimating the waveformafter the interpolation. In embodiments, the vibration data can belooped to purposely emulate conventional tape recorder loops, withdigital filtering techniques used with the effective splice tofacilitate longer analyses. It will be appreciated in light of thedisclosure that the above techniques do not preclude waveform, spectrum,and other types of analyses to be processed and displayed with a GUI ofthe user at the time of collection. It will be appreciated in light ofthe disclosure that newer systems can permit this functionality to beperformed in parallel to the high-performance collection of the rawwaveform data.

With respect to time of collection issues, it will be appreciated thatolder systems using the compromised approach of improving dataresolution, by collecting at different sampling rates and data lengths,do not in fact save as much time as expected. To that end, every timethe data acquisition hardware is stopped and started, latency issues canbe created, especially when there is hardware auto-scaling performed.The same can be true with respect to data retrieval of the routeinformation (i.e., test locations) that is often in a database formatand can be exceedingly slow. The storage of the raw data in bursts todisk (whether solid state or otherwise) can also be undesirably slow.

In contrast, the many embodiments include digitally streaming thewaveform data 2010, as disclosed herein, and also enjoying the benefitof needing to load the route parameter information while setting thedata acquisition hardware only once. Because the waveform data 2010 isstreamed to only one file, there is no need to open and close files, orswitch between loading and writing operations with the storage medium.It can be shown that the collection and storage of the waveform data2010, as described herein, can be shown to produce relatively moremeaningful data in significantly less time than the traditional batchdata acquisition approach. An example of this includes an electric motorabout which waveform data can be collected with a data length of 4Kpoints (i.e., 4,096) for sufficiently high resolution in order to, amongother things, distinguish electrical sideband frequencies. For fans orblowers, a reduced resolution of 1K (i.e., 1,024) can be used. Incertain instances, 1K can be the minimum waveform data lengthrequirement. The sampling rate can be 1,280 Hz and that equates to anFmax of 500 Hz. It will be appreciated in light of the disclosure thatoversampling by an industry standard factor of 2.56 can satisfy thenecessary two-times (2×) oversampling for the Nyquist Criterion withsome additional leeway that can accommodate anti-aliasingfilter-rolloff. The time to acquire this waveform data would be 1,024points at 1,280 hertz, which are 800 milliseconds.

To improve accuracy, the waveform data can be averaged. Eight averagescan be used with, for example, fifty percent overlap. This would extendthe time from 800 milliseconds to 3.6 seconds, which is equal to 800msec×8 averages×0.5 (overlap ratio)+0.5×800 msec (non-overlapped headand tail ends). After collection at Fmax=500 Hz waveform data, a highersampling rate can be used. In one example, ten times (10×) the previoussampling rate can be used and Fmax=10 kHz. By way of this example, eightaverages can be used with fifty percent (50%) overlap to collectwaveform data at this higher rate that can amount to a collection timeof 360 msec or 0.36 seconds. It will be appreciated in light of thedisclosure that it can be necessary to read the hardware collectionparameters for the higher sampling rate from the route list, as well aspermit hardware auto-scaling, or the resetting of other necessaryhardware collection parameters, or both. To that end, a few seconds oflatency can be added to accommodate the changes in sampling rate. Inother instances, introducing latency can accommodate hardwareautoscaling and changes to hardware collection parameters that can berequired when using the lower sampling rate disclosed herein. Inaddition to accommodating the change in sampling rate, additional timeis needed for reading the route point information from the database(i.e., where to monitor and where to monitor next), displaying the routeinformation, and processing the waveform data. Moreover, display of thewaveform data and/or associated spectra can also consume significanttime. In light of the above, 15 seconds to 20 seconds can elapse whileobtaining waveform data at each measurement point.

In further examples, additional sampling rates can be added but this canmake the total amount time for the vibration survey even longer becausetime adds up from changeover time from one sampling rate to another andfrom the time to obtain additional data at different sampling rate. Inone example, a lower sampling rate is used, such as a sampling rate of128 Hz where Fmax=50 Hz. By way of this example, the vibration surveywould, therefore, require an additional 36 seconds for the first set ofaveraged data at this sampling rate, in addition to others mentionedabove, and consequently the total time spent at each measurement pointincreases even more dramatically. Further embodiments include usingsimilar digital streaming of gap free waveform data as disclosed hereinfor use with wind turbines and other machines that can have relativelyslow speed rotating or oscillating systems. In many examples, thewaveform data collected can include long samples of data at a relativelyhigh-sampling rate. In one example, the sampling rate can be 100 kHz andthe sampling duration can be for two minutes on all of the channelsbeing recorded. In many examples, one channel can be for the single axisreference sensor and three more data channels can be for the tri-axialthree channel sensor. It will be appreciated in light of the disclosurethat the long data length can be shown to facilitate detection ofextremely low frequency phenomena. The long data length can also beshown to accommodate the inherent speed variability in wind turbineoperations. Additionally, the long data length can further be shown toprovide the opportunity for using numerous averages such as thosediscussed herein, to achieve very high spectral resolution, and to makefeasible tape loops for certain spectral analyses. Many multipleadvanced analytical techniques can now become available because suchtechniques can use the available long uninterrupted length of waveformdata in accordance with the present disclosure.

It will also be appreciated in light of the disclosure that thesimultaneous collection of waveform data from multiple channels canfacilitate performing transfer functions between multiple channels.Moreover, the simultaneous collection of waveform data from multiplechannels facilitates establishing phase relationships across the machineso that more sophisticated correlations can be utilized by relying onthe fact that the waveforms from each of the channels are collectedsimultaneously. In other examples, more channels in the data collectioncan be used to reduce the time it takes to complete the overallvibration survey by allowing for simultaneous acquisition of waveformdata from multiple sensors that otherwise would have to be acquired, ina subsequent fashion, moving sensor to sensor in the vibration survey.

The present disclosure includes the use of at least one of thesingle-axis reference probes on one of the channels to allow foracquisition of relative phase comparisons between channels. Thereference probe can be an accelerometer or other type of transducer thatis not moved and, therefore, fixed at an unchanging location during thevibration survey of one machine. Multiple reference probes can each bedeployed as at suitable locations fixed in place (i.e., at unchanginglocations) throughout the acquisition of vibration data during thevibration survey. In certain examples, up to seven reference probes canbe deployed depending on the capacity of the data collection module 2160or the like. Using transfer functions or similar techniques, therelative phases of all channels may be compared with one another at allselected frequencies. By keeping the one or more reference probes fixedat their unchanging locations while moving or monitoring the othertri-axial vibration sensors, it can be shown that the entire machine canbe mapped with regard to amplitude and relative phase. This can be shownto be true even when there are more measurement points than channels ofdata collection. With this information, an operating deflection shapecan be created that can show dynamic movements of the machine in 3 D,which can provide an invaluable diagnostic tool. In embodiments, the oneor more reference probes can provide relative phase, rather thanabsolute phase. It will be appreciated in light of the disclosure thatrelative phase may not be as valuable absolute phase for some purposes,but the relative phase the information can still be shown to be veryuseful.

In embodiments, the sampling rates used during the vibration survey canbe digitally synchronized to predetermined operational frequencies thatcan relate to pertinent parameters of the machine such as rotating oroscillating speed. Doing this, permits extracting even more informationusing synchronized averaging techniques. It will be appreciated in lightof the disclosure that this can be done without the use of a key phasoror a reference pulse from a rotating shaft, which is usually notavailable for route collected data. As such, non-synchronous signals canbe removed from a complex signal without the need to deploy synchronousaveraging using the key phasor. This can be shown to be very powerfulwhen analyzing a particular pinion in a gearbox or generally applied toany component within a complicated mechanical mechanism. In manyinstances, the key phasor or the reference pulse is rarely availablewith route collected data, but the techniques disclosed herein canovercome this absence. In embodiments, there can be multiple shaftsrunning at different speeds within the machine being analyzed. Incertain instances, there can be a single-axis reference probe for eachshaft. In other instances, it is possible to relate the phase of oneshaft to another shaft using only one single axis reference probe on oneshaft at its unchanging location. In embodiments, variable speedequipment can be more readily analyzed with relatively longer durationof data relative to single speed equipment. The vibration survey can beconducted at several machine speeds within the same contiguous set ofvibration data using the same techniques disclosed herein. Thesetechniques can also permit the study of the change of the relationshipbetween vibration and the change of the rate of speed that was notavailable before.

In embodiments, there are numerous analytical techniques that can emergefrom because raw waveform data can be captured in a gap-free digitalformat as disclosed herein. The gap-free digital format can facilitatemany paths to analyze the waveform data in many ways after the fact toidentify specific problems. The vibration data collected in accordancewith the techniques disclosed herein can provide the analysis oftransient, semi-periodic and very low frequency phenomena. The waveformdata acquired in accordance with the present disclosure can containrelatively longer streams of raw gap-free waveform data that can beconveniently played back as needed, and on which many and variedsophisticated analytical techniques can be performed. A large number ofsuch techniques can provide for various forms of filtering to extractlow amplitude modulations from transient impact data that can beincluded in the relatively longer stream of raw gap-free waveform data.It will be appreciated in light of the disclosure that in past datacollection practices, these types of phenomena were typically lost bythe averaging process of the spectral processing algorithms because thegoal of the previous data acquisition module was purely periodicsignals; or these phenomena were lost to file size reductionmethodologies due to the fact that much of the content from an originalraw signal was typically discarded knowing it would not be used.

In embodiments, there is a method of monitoring vibration of a machinehaving at least one shaft supported by a set of bearings. The methodincludes monitoring a first data channel assigned to a single-axissensor at an unchanging location associated with the machine. The methodalso includes monitoring a second, third, and fourth data channelassigned to a three-axis sensor. The method further includes recordinggap-free digital waveform data simultaneously from all of the datachannels while the machine is in operation; and determining a change inrelative phase based on the digital waveform data. The method alsoincludes the tri-axial sensor being located at a plurality of positionsassociated with the machine while obtaining the digital waveform. Inembodiments, the second, third, and fourth channels are assignedtogether to a sequence of tri-axial sensors each located at differentpositions associated with the machine. In embodiments, the data isreceived from all of the sensors on all of their channelssimultaneously.

The method also includes determining an operating deflection shape basedon the change in relative phase information and the waveform data. Inembodiments, the unchanging location of the reference sensor is aposition associated with a shaft of the machine. In embodiments, thetri-axial sensors in the sequence of the tri-axial sensors are eachlocated at different positions and are each associated with differentbearings in the machine. In embodiments, the unchanging location is aposition associated with a shaft of the machine and the tri-axialsensors in the sequence of the tri-axial sensors are each located atdifferent positions and are each associated with different bearings thatsupport the shaft in the machine. The various embodiments includemethods of sequentially monitoring vibration or similar processparameters and signals of a rotating or oscillating machine or analogousprocess machinery from a number of channels simultaneously, which can beknown as an ensemble. In various examples, the ensemble can include oneto eight channels. In further examples, an ensemble can represent alogical measurement grouping on the equipment being monitored whetherthose measurement locations are temporary for measurement, supplied bythe original equipment manufacturer, retrofit at a later date, or one ormore combinations thereof.

In one example, an ensemble can monitor bearing vibration in a singledirection. In a further example, an ensemble can monitor three differentdirections (e.g., orthogonal directions) using a tri-axial sensor. Inyet further examples, an ensemble can monitor four or more channelswhere the first channel can monitor a single axis vibration sensor, andthe second, the third, and the fourth channels can monitor each of thethree directions of the tri-axial sensor. In other examples, theensemble can be fixed to a group of adjacent bearings on the same pieceof equipment or an associated shaft. The various embodiments providemethods that include strategies for collecting waveform data fromvarious ensembles deployed in vibration studies or the like in arelatively more efficient manner. The methods also includesimultaneously monitoring of a reference channel assigned to anunchanging reference location associated with the ensemble monitoringthe machine. The cooperation with the reference channel can be shown tosupport a more complete correlation of the collected waveforms from theensembles. The reference sensor on the reference channel can be a singleaxis vibration sensor, or a phase reference sensor that can be triggeredby a reference location on a rotating shaft or the like. As disclosedherein, the methods can further include recording gap-free digitalwaveform data simultaneously from all of the channels of each ensembleat a relatively high rate of sampling so as to include all frequenciesdeemed necessary for the proper analysis of the machinery beingmonitored while it is in operation. The data from the ensembles can bestreamed gap-free to a storage medium for subsequent processing that canbe connected to a cloud network facility, a local data link, Bluetooth™connectivity, cellular data connectivity, or the like.

In embodiments, the methods disclosed herein include strategies forcollecting data from the various ensembles including digital signalprocessing techniques that can be subsequently applied to data from theensembles to emphasize or better isolate specific frequencies orwaveform phenomena. This can be in contrast with current methods thatcollect multiple sets of data at different sampling rates, or withdifferent hardware filtering configurations including integration, thatprovide relatively less post-processing flexibility because of thecommitment to these same (known as a priori hardware configurations).These same hardware configurations can also be shown to increase time ofthe vibration survey due to the latency delays associated withconfiguring the hardware for each independent test. In embodiments, themethods for collecting data from various ensembles include data markertechnology that can be used for classifying sections of streamed data ashomogenous and belonging to a specific ensemble. In one example, aclassification can be defined as operating speed. In doing so, amultitude of ensembles can be created from what conventional systemswould collect as only one. The many embodiments include post-processinganalytic techniques for comparing the relative phases of all thefrequencies of interest not only between each channel of the collectedensemble but also between all of the channels of all of the ensemblesbeing monitored, when applicable.

With reference to FIG. 12, the many embodiments include a first machine2400 having rotating or oscillating components 2410, or both, eachsupported by a set of bearings 2420 including a bearing pack 2422, abearing pack 2424, a bearing pack 2426, and more as needed. The firstmachine 2400 can be monitored by a first sensor ensemble 2450. The firstensemble 2450 can be configured to receive signals from sensorsoriginally installed (or added later) on the first machine 2400. Thesensors on the machine 2400 can include single-axis sensors 2460, suchas a single-axis sensor 2462, a single-axis sensor 2464, and more asneeded. In many examples, the single axis-sensors 2460 can be positionedin the machine 2400 at locations that allow for the sensing of one ofthe rotating or oscillating components 2410 of the machine 2400.

The machine 2400 can also have tri-axial (e.g., orthogonal axes) sensors2480, such as a tri-axial sensor 2482, a tri-axial sensor 2484, and moreas needed. In many examples, the tri-axial sensors 2480 can bepositioned in the machine 2400 at locations that allow for the sensingof one of each of the bearing packs in the sets of bearings 2420 that isassociated with the rotating or oscillating components of the machine2400. The machine 2400 can also have temperature sensors 2500, such as atemperature sensor 2502, a temperature sensor 2504, and more as needed.The machine 2400 can also have a tachometer sensor 2510 or more asneeded that each detail the RPMs of one of its rotating components. Byway of the above example, the first sensor ensemble 2450 can survey theabove sensors associated with the first machine 2400. To that end, thefirst ensemble 2450 can be configured to receive eight channels. Inother examples, the first sensor ensemble 2450 can be configured to havemore than eight channels, or less than eight channels as needed. In thisexample, the eight channels include two channels that can each monitor asingle-axis reference sensor signal and three channels that can monitora tri-axial sensor signal. The remaining three channels can monitor twotemperature signals and a signal from a tachometer. In one example, thefirst ensemble 2450 can monitor the single-axis sensor 2462, thesingle-axis sensor 2464, the tri-axial sensor 2482, the temperaturesensor 2502, the temperature sensor 2504, and the tachometer sensor 2510in accordance with the present disclosure. During a vibration survey onthe machine 2400, the first ensemble 2450 can first monitor thetri-axial sensor 2482 and then move next to the tri-axial sensor 2484.

After monitoring the tri-axial sensor 2484, the first ensemble 2450 canmonitor additional tri-axial sensors on the machine 2400 as needed andthat are part of the predetermined route list associated with thevibration survey of the machine 2400, in accordance with the presentdisclosure. During this vibration survey, the first ensemble 2450 cancontinually monitor the single-axis sensor 2462, the single-axis sensor2464, the two temperature sensors 2502, 2504, and the tachometer sensor2510 while the first ensemble 2450 can serially monitor the multipletri-axial sensors 2480 in the pre-determined route plan for thisvibration survey.

With reference to FIG. 12, the many embodiments include a second machine2600 having rotating or oscillating components 2610, or both, eachsupported by a set of bearings 2620 including a bearing pack 2622, abearing pack 2624, a bearing pack 2626, and more as needed. The secondmachine 2600 can be monitored by a second sensor ensemble 2650. Thesecond ensemble 2650 can be configured to receive signals from sensorsoriginally installed (or added later) on the second machine 2600. Thesensors on the machine 2600 can include single-axis sensors 2660, suchas a single-axis sensor 2662, a single-axis sensor 2664, and more asneeded. In many examples, the single axis-sensors 2660 can be positionedin the machine 2600 at locations that allow for the sensing of one ofthe rotating or oscillating components 2610 of the machine 2600.

The machine 2600 can also have tri-axial (e.g., orthogonal axes) sensors2680, such as a tri-axial sensor 2682, a tri-axial sensor 2684, atri-axial sensor 2686, a tri-axial sensor 2688, and more as needed. Inmany examples, the tri-axial sensors 2680 can be positioned in themachine 2600 at locations that allow for the sensing of one of each ofthe bearing packs in the sets of bearings 2620 that is associated withthe rotating or oscillating components of the machine 2600. The machine2600 can also have temperature sensors 2700, such as a temperaturesensor 2702, a temperature sensor 2704, and more as needed. The machine2600 can also have a tachometer sensor 2710 or more as needed that eachdetail the RPMs of one of its rotating components.

By way of the above example, the second sensor ensemble 2650 can surveythe above sensors associated with the second machine 2600. To that end,the second ensemble 2650 can be configured to receive eight channels. Inother examples, the second sensor ensemble 2650 can be configured tohave more than eight channels or less than eight channels as needed. Inthis example, the eight channels include one channel that can monitor asingle-axis reference sensor signal and six channels that can monitortwo tri-axial sensor signals. The remaining channel can monitor atemperature signal. In one example, the second ensemble 2650 can monitorthe single axis sensor 2662, the tri-axial sensor 2682, the tri-axialsensor 2684, and the temperature sensor 2702. During a vibration surveyon the machine 2600 in accordance with the present disclosure, thesecond ensemble 2650 can first monitor the tri-axial sensor 2682simultaneously with the tri-axial sensor 2684 and then move onto thetri-axial sensor 2686 simultaneously with the tri-axial sensor 2688.

After monitoring the tri-axial sensors 2680, the second ensemble 2650can monitor additional tri-axial sensors (in simultaneous pairs) on themachine 2600 as needed and that are part of the predetermined route listassociated with the vibration survey of the machine 2600 in accordancewith the present disclosure. During this vibration survey, the secondensemble 2650 can continually monitor the single-axis sensor 2662 at itsunchanging location and the temperature sensor 2702 while the secondensemble 2650 can serially monitor the multiple tri-axial sensors in thepre-determined route plan for this vibration survey.

With continuing reference to FIG. 12, the many embodiments include athird machine 2800 having rotating or oscillating components 2810, orboth, each supported by a set of bearings 2820 including a bearing pack2822, a bearing pack 2824, a bearing pack 2826, and more as needed. Thethird machine 2800 can be monitored by a third sensor ensemble 2850. Thethird ensemble 2850 can be configured with a single-axis sensor 2860,and two tri-axial (e.g., orthogonal axes) sensors 2880, 2882. In manyexamples, the single axis-sensor 2860 can be secured by the user on themachine 2800 at a location that allows for the sensing of one of therotating or oscillating components of the machine 2800. The tri-axialsensors 2880, 2882 can be also be located on the machine 2800 by theuser at locations that allow for the sensing of one of each of thebearings in the sets of bearings that each associated with the rotatingor oscillating components of the machine 2800. The third ensemble 2850can also include a temperature sensor 2900. The third ensemble 2850 andits sensors can be moved to other machines unlike the first and secondensembles 2450, 2650.

The many embodiments also include a fourth machine 2950 having rotatingor oscillating components 2960, or both, each supported by a set ofbearings 2970 including a bearing pack 2972, a bearing pack 2974, abearing pack 2976, and more as needed. The fourth machine 2950 can bealso monitored by the third sensor ensemble 2850 when the user moves itto the fourth machine 2950. The many embodiments also include a fifthmachine 3000 having rotating or oscillating components 3010, or both.The fifth machine 3000 may not be explicitly monitored by any sensor orany sensor ensembles in operation but it can create vibrations or otherimpulse energy of sufficient magnitude to be recorded in the dataassociated with any one of the machines 2400, 2600, 2800, 2950 under avibration survey.

The many embodiments include monitoring the first sensor ensemble 2450on the first machine 2400 through the predetermined route as disclosedherein. The many embodiments also include monitoring the second sensorensemble 2650 on the second machine 2600 through the predeterminedroute. The locations of machine 2400 being close to machine 2600 can beincluded in the contextual metadata of both vibration surveys. The thirdensemble 2850 can be moved between machine 2800, machine 2950, and othersuitable machines. The machine 3000 has no sensors onboard as configuredbut could be monitored as needed by the third sensor ensemble 2850. Themachine 3000 and its operational characteristics can be recorded in themetadata in relation to the vibration surveys on the other machines tonote its contribution due to its proximity.

The many embodiments include hybrid database adaptation for harmonizingrelational metadata and streaming raw data formats. Unlike older systemsthat utilized traditional database structure for associating nameplateand operational parameters (sometimes deemed metadata) with individualdata measurements that are discrete and relatively simple, it will beappreciated in light of the disclosure that more modern systems cancollect relatively larger quantities of raw streaming data with highersampling rates and greater resolutions. At the same time, it will alsobe appreciated in light of the disclosure that the network of metadatawith which to link and obtain this raw data or correlate with this rawdata, or both, is expanding at ever-increasing rates.

In one example, a single overall vibration level can be collected aspart of a route or prescribed list of measurement points. This datacollected can then be associated with database measurement locationinformation for a point located on a surface of a bearing housing on aspecific piece of the machine adjacent to a coupling in a verticaldirection. Machinery analysis parameters relevant to the proper analysiscan be associated with the point located on the surface. Examples ofmachinery analysis parameters relevant to the proper analysis caninclude a running speed of a shaft passing through the measurement pointon the surface. Further examples of machinery analysis parametersrelevant to the proper analysis can include one of, or a combination of:running speeds of all component shafts for that piece of equipmentand/or machine, bearing types being analyzed such as sleeve or rollingelement bearings, the number of gear teeth on gears should there be agearbox, the number of poles in a motor, slip and line frequency of amotor, roller bearing element dimensions, number of fan blades, or thelike. Examples of machinery analysis parameters relevant to the properanalysis can further include machine operating conditions such as theload on the machines and whether load is expressed in percentage,wattage, air flow, head pressure, horsepower, and the like. Furtherexamples of machinery analysis parameters include information relevantto adjacent machines that might influence the data obtained during thevibration study.

It will be appreciated in light of the disclosure that the vast array ofequipment and machinery types can support many differentclassifications, each of which can be analyzed in distinctly differentways. For example, some machines, like screw compressors and hammermills, can be shown to run much noisier and can be expected to vibratesignificantly more than other machines. Machines known to vibrate moresignificantly can be shown to require a change in vibration levels thatcan be considered acceptable relative to quieter machines.

The present disclosure further includes hierarchical relationships foundin the vibrational data collected that can be used to support properanalysis of the data. One example of the hierarchical data includes theinterconnection of mechanical componentry such as a bearing beingmeasured in a vibration survey and the relationship between thatbearing, including how that bearing connects to a particular shaft onwhich is mounted a specific pinion within a particular gearbox, and therelationship between the shaft, the pinion, and the gearbox. Thehierarchical data can further include in what particular spot within amachinery gear train that the bearing being monitored is locatedrelative to other components in the machine. The hierarchical data canalso detail whether the bearing being measured in a machine is in closeproximity to another machine whose vibrations may affect what is beingmeasured in the machine that is the subject of the vibration study.

The analysis of the vibration data from the bearing or other componentsrelated to one another in the hierarchical data can use table lookups,searches for correlations between frequency patterns derived from theraw data, and specific frequencies from the metadata of the machine. Insome embodiments, the above can be stored in and retrieved from arelational database. In embodiments, National Instrument's TechnicalData Management Solution (TDMS) file format can be used. The TDMS fileformat can be optimized for streaming various types of measurement data(i.e., binary digital samples of waveforms), as well as also being ableto handle hierarchical metadata.

The many embodiments include a hybrid relational metadata-binary storageapproach (HRM-BSA). The HRM-BSA can include a structured query language(SQL) based relational database engine. The structured query languagebased relational database engine can also include a raw data engine thatcan be optimized for throughput and storage density for data that isflat and relatively structureless. It will be appreciated in light ofthe disclosure that benefits can be shown in the cooperation between thehierarchical metadata and the SQL relational database engine. In oneexample, marker technologies and pointer sign-posts can be used to makecorrelations between the raw database engine and the SQL relationaldatabase engine. Three examples of correlations between the raw databaseengine and the SQL relational database engine linkages include: (1)pointers from the SQL database to the raw data; (2) pointers from theancillary metadata tables or similar grouping of the raw data to the SQLdatabase; and (3) independent storage tables outside the domain ofeither the SQL database or raw data technologies.

With reference to FIG. 13, the present disclosure can include pointersfor Group 1 and Group 2 that can include associated filenames, pathinformation, table names, database key fields as employed with existingSQL database technologies that can be used to associate a specificdatabase segments or locations, asset properties to specific measurementraw data streams, records with associated time/date stamps, orassociated metadata such as operating parameters, panel conditions, andthe like. By way of this example, a plant 3200 can include a machine one3202, a machine two 3204, and many others in the plant 3200. The machineone 3202 can include a gearbox 3210, a motor 3212, and other elements.The machine two 3204 can include a motor 3220, and other elements. Manywaveforms 3230 including waveform 3240, waveform 3242, waveform 3244,and additional waveforms as needed can be acquired from the machines3202, 3204 in the plant 3200. The waveforms 3230 can be associated withthe local marker linking tables 3300 and a linking raw data tables 3400.The machines 3202, 3204 and their elements can be associated withlinking tables having relational databases 3500. The linking tables rawdata tables 3400 and the linking tables having relational databases 3500can be associated with the linking tables with optional independentstorage tables 3600.

The present disclosure can include markers that can be applied to a timemark or a sample length within the raw waveform data. The markersgenerally fall into two categories: preset or dynamic. The presetmarkers can correlate to preset or existing operating conditions (e.g.,load, head pressure, air flow cubic feet per minute, ambienttemperature, RPMs, and the like.). These preset markers can be fed intothe data acquisition system directly. In certain instances, the presetmarkers can be collected on data channels in parallel with the waveformdata (e.g., waveforms for vibration, current, voltage, etc.).Alternatively, the values for the preset markers can be enteredmanually.

For dynamic markers such as trending data, it can be important tocompare similar data like comparing vibration amplitudes and patternswith a repeatable set of operating parameters. One example of thepresent disclosure includes one of the parallel channel inputs being akey phasor trigger pulse from an operating shaft that can provide RPMinformation at the instantaneous time of collection. In this example ofdynamic markers, sections of collected waveform data can be marked withappropriate speeds or speed ranges.

The present disclosure can also include dynamic markers that cancorrelate to data that can be derived from post processing and analyticsperformed on the sample waveform. In further embodiments, the dynamicmarkers can also correlate to post-collection derived parametersincluding RPMs, as well as other operationally derived metrics such asalarm conditions like a maximum RPM. In certain examples, many modernpieces of equipment that are candidates for a vibration survey with theportable data collection systems described herein do not includetachometer information. This can be true because it is not alwayspractical or cost-justifiable to add a tachometer even though themeasurement of RPM can be of primary importance for the vibration surveyand analysis. It will be appreciated that for fixed speed machineryobtaining an accurate RPM measurement can be less important especiallywhen the approximate speed of the machine can be ascertainedbefore-hand; however, variable-speed drives are becoming more and moreprevalent. It will also be appreciated in light of the disclosure thatvarious signal processing techniques can permit the derivation of RPMfrom the raw data without the need for a dedicated tachometer signal.

In many embodiments, the RPM information can be used to mark segments ofthe raw waveform data over its collection history. Further embodimentsinclude techniques for collecting instrument data following a prescribedroute of a vibration study. The dynamic markers can enable analysis andtrending software to utilize multiple segments of the collectioninterval indicated by the markers (e.g., two minutes) as multiplehistorical collection ensembles, rather than just one as done inprevious systems where route collection systems would historically storedata for only one RPM setting. This could, in turn, be extended to anyother operational parameter such as load setting, ambient temperature,and the like, as previously described. The dynamic markers, however,that can be placed in a type of index file pointing to the raw datastream can classify portions of the stream in homogenous entities thatcan be more readily compared to previously collected portions of the rawdata stream.

The many embodiments include the hybrid relational metadata-binarystorage approach that can use the best of pre-existing technologies forboth relational and raw data streams. In embodiments, the hybridrelational metadata-binary storage approach can marry them together witha variety of marker linkages. The marker linkages can permit rapidsearches through the relational metadata and can allow for moreefficient analyses of the raw data using conventional SQL techniqueswith pre-existing technology. This can be shown to permit utilization ofmany of the capabilities, linkages, compatibilities, and extensions thatconventional database technologies do not provide.

The marker linkages can also permit rapid and efficient storage of theraw data using conventional binary storage and data compressiontechniques. This can be shown to permit utilization of many of thecapabilities, linkages, compatibilities, and extensions thatconventional raw data technologies provide such as TDMS (NationalInstruments), UFF (Universal File Format such as UFF58), and the like.The marker linkages can further permit using the marker technology linkswhere a vastly richer set of data from the ensembles can be amassed inthe same collection time as more conventional systems. The richer set ofdata from the ensembles can store data snapshots associated withpredetermined collection criterion and the proposed system can derivemultiple snapshots from the collected data streams utilizing the markertechnology. In doing so, it can be shown that a relatively richeranalysis of the collected data can be achieved. One such benefit caninclude more trending points of vibration at a specific frequency ororder of running speed versus RPM, load, operating temperature, flowrates, and the like, which can be collected for a similar time relativeto what is spent collecting data with a conventional system.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals frommachines, elements of the machines and the environment of the machinesincluding heavy duty machines deployed at a local job site or atdistributed job sites under common control. The heavy-duty machines mayinclude earthmoving equipment, heavy duty on-road industrial vehicles,heavy duty off-road industrial vehicles, industrial machines deployed invarious settings such as turbines, turbomachinery, generators, pumps,pulley systems, manifold and valve systems, and the like. Inembodiments, heavy industrial machinery may also include earth-movingequipment, earth-compacting equipment, hauling equipment, hoistingequipment, conveying equipment, aggregate production equipment,equipment used in concrete construction, and piledriving equipment. Inexamples, earth moving equipment may include excavators, backhoes,loaders, bulldozers, skid steer loaders, trenchers, motor graders, motorscrapers, crawler loaders, and wheeled loading shovels. In examples,construction vehicles may include dumpers, tankers, tippers, andtrailers. In examples, material handling equipment may include cranes,conveyors, forklift, and hoists. In examples, construction equipment mayinclude tunnel and handling equipment, road rollers, concrete mixers,hot mix plants, road making machines (compactors), stone crashers,pavers, slurry seal machines, spraying and plastering machines, andheavy-duty pumps. Further examples of heavy industrial equipment mayinclude different systems such as implement traction, structure, powertrain, control, and information. Heavy industrial equipment may includemany different powertrains and combinations thereof to provide power forlocomotion and to also provide power to accessories and onboardfunctionality. In each of these examples, the platform 100 may deploythe local data collection system 102 into the environment 104 in whichthese machines, motors, pumps, and the like, operate and directlyconnected integrated into each of the machines, motors, pumps, and thelike.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals frommachines in operation and machines in being constructed such as turbineand generator sets like a Siemens™ SGT6-5000F™ gas turbine, an SST-900™steam turbine, a SGen6-1000A™ generator, and a SGen6-100A™ generator,and the like. In embodiments, the local data collection system 102 maybe deployed to monitor steam turbines as they rotate in the currentscaused by hot water vapor that may be directed through the turbine butotherwise generated from a different source such as from gas-firedburners, nuclear cores, molten salt loops and the like. In thesesystems, the local data collection system 102 may monitor the turbinesand the water or other fluids in a closed loop cycle in which watercondenses and is then heated until it evaporates again. The local datacollection system 102 may monitor the steam turbines separately from thefuel source deployed to heat the water to steam. In examples, workingtemperatures of steam turbines may be between 500 and 650° C. In manyembodiments, an array of steam turbines may be arranged and configuredfor high, medium, and low pressure, so they may optimally convert therespective steam pressure into rotational movement.

The local data collection system 102 may also be deployed in a gasturbines arrangement and therefore not only monitor the turbine inoperation but also monitor the hot combustion gases feed into theturbine that may be in excess of 1,500° C. Because these gases are muchhotter than those in steam turbines, the blades may be cooled with airthat may flow out of small openings to create a protective film orboundary layer between the exhaust gases and the blades. Thistemperature profile may be monitored by the local data collection system102. Gas turbine engines, unlike typical steam turbines, include acompressor, a combustion chamber, and a turbine all of which arejournaled for rotation with a rotating shaft. The construction andoperation of each of these components may be monitored by the local datacollection system 102.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals from waterturbines serving as rotary engines that may harvest energy from movingwater and are used for electric power generation. The type of waterturbine or hydro-power selected for a project may be based on the heightof standing water, often referred to as head, and the flow (or volume ofwater) at the site. In this example, a generator may be placed at thetop of a shaft that connects to the water turbine. As the turbinecatches the naturally moving water in its blade and rotates, the turbinesends rotational power to the generator to generate electrical energy.In doing so, the platform 100 may monitor signals from the generators,the turbines, the local water system, flow controls such as dam windowsand sluices. Moreover, the platform 100 may monitor local conditions onthe electric grid including load, predicted demand, frequency response,and the like, and include such information in the monitoring and controldeployed by platform 100 in these hydroelectric settings.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromenergy production environments, such as thermal, nuclear, geothermal,chemical, biomass, carbon-based fuels, hybrid-renewable energy plants,and the like. Many of these plants may use multiple forms of energyharvesting equipment like wind turbines, hydro turbines, and steamturbines powered by heat from nuclear, gas-fired, solar, and molten saltheat sources. In embodiments, elements in such systems may includetransmission lines, heat exchangers, desulphurization scrubbers, pumps,coolers, recuperators, chillers, and the like. In embodiments, certainimplementations of turbomachinery, turbines, scroll compressors, and thelike may be configured in arrayed control so as to monitor largefacilities creating electricity for consumption, providingrefrigeration, creating steam for local manufacture and heating, and thelike, and that arrayed control platforms may be provided by the providerof the industrial equipment such as Honeywell and their Experion™ PKSplatform. In embodiments, the platform 100 may specifically communicatewith and integrate the local manufacturer-specific controls and mayallow equipment from one manufacturer to communicate with otherequipment. Moreover, the platform 100 provides allows for the local datacollection system 102 to collect information across systems from manydifferent manufacturers. In embodiments, the platform 100 may includethe local data collection system 102 deployed in the environment 104 tomonitor signals from marine industrial equipment, marine diesel engines,shipbuilding, oil and gas plants, refineries, petrochemical plant,ballast water treatment solutions, marine pumps and turbines, and thelike.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals from heavyindustrial equipment and processes including monitoring one or moresensors. By way of this example, sensors may be devices that may be usedto detect or respond to some type of input from a physical environment,such as an electrical, heat, or optical signal. In embodiments, thelocal data collection system 102 may include multiple sensors such as,without limitation, a temperature sensor, a pressure sensor, a torquesensor, a flow sensor, a heat sensor, a smoke sensor, an arc sensor, aradiation sensor, a position sensor, an acceleration sensor, a strainsensor, a pressure cycle sensor, a pressure sensor, an air temperaturesensor, and the like. The torque sensor may encompass a magnetic twistangle sensor. In one example, the torque and speed sensors in the localdata collection system 102 may be similar to those discussed in U.S.Pat. No. 8,352,149 to Meachem, issued 8 Jan. 2013 and herebyincorporated by reference as if fully set forth herein. In embodiments,one or more sensors may be provided such as a tactile sensor, abiosensor, a chemical sensor, an image sensor, a humidity sensor, aninertial sensor, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromsensors that may provide signals for fault detection including excessivevibration, incorrect material, incorrect material properties, truenessto the proper size, trueness to the proper shape, proper weight,trueness to balance. Additional fault sensors include those forinventory control and for inspections such as to confirm that parts arepackaged to plan, parts are to tolerance in a plan, occurrence ofpackaging damage or stress, and sensors that may indicate the occurrenceof shock or damage in transit. Additional fault sensors may includedetection of the lack of lubrication, over lubrication, the need forcleaning of the sensor detection window, the need for maintenance due tolow lubrication, the need for maintenance due to blocking or reducedflow in a lubrication region, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 that includes aircraftoperations and manufacture including monitoring signals from sensors forspecialized applications such as sensors used in an aircraft's Attitudeand Heading Reference System (AHRS), such as gyroscopes, accelerometers,and magnetometers. In embodiments, the platform 100 may include thelocal data collection system 102 deployed in the environment 104 tomonitor signals from image sensors such as semiconductor charge coupleddevices (CCDs), active pixel sensors, in complementarymetal-oxide-semiconductor (CMOS) or N-type metal-oxide-semiconductor(NMOS, Live MOS) technologies. In embodiments, the platform 100 mayinclude the local data collection system 102 deployed in the environment104 to monitor signals from sensors such as an infra-red (IR) sensor, anultraviolet (UV) sensor, a touch sensor, a proximity sensor, and thelike. In embodiments, the platform 100 may include the local datacollection system 102 deployed in the environment 104 to monitor signalsfrom sensors configured for optical character recognition (OCR), readingbarcodes, detecting surface acoustic waves, detecting transponders,communicating with home automation systems, medical diagnostics, healthmonitoring, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromsensors such as a Micro-Electro-Mechanical Systems (MEMS) sensor, suchas ST Microelectronic's™ LSM303AH smart MEMS sensor, which may includean ultra-low-power high-performance system-in-package featuring a 3Ddigital linear acceleration sensor and a 3D digital magnetic sensor.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromadditional large machines such as turbines, windmills, industrialvehicles, robots, and the like. These large mechanical machines includemultiple components and elements providing multiple subsystems on eachmachine. To that end, the platform 100 may include the local datacollection system 102 deployed in the environment 104 to monitor signalsfrom individual elements such as axles, bearings, belts, buckets, gears,shafts, gear boxes, cams, carriages, camshafts, clutches, brakes, drums,dynamos, feeds, flywheels, gaskets, pumps, jaws, robotic arms, seals,sockets, sleeves, valves, wheels, actuators, motors, servomotor, and thelike. Many of the machines and their elements may include servomotors.The local data collection system 102 may monitor the motor, the rotaryencoder, and the potentiometer of the servomechanism to providethree-dimensional detail of position, placement, and progress ofindustrial processes.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals from geardrives, powertrains, transfer cases, multispeed axles, transmissions,direct drives, chain drives, belt-drives, shaft-drives, magnetic drives,and similar meshing mechanical drives. In embodiments, the platform 100may include the local data collection system 102 deployed in theenvironment 104 to monitor signals from fault conditions of industrialmachines that may include overheating, noise, grinding gears, lockedgears, excessive vibration, wobbling, under-inflation, over-inflation,and the like. Operation faults, maintenance indicators, and interactionsfrom other machines may cause maintenance or operational issues mayoccur during operation, during installation, and during maintenance. Thefaults may occur in the mechanisms of the industrial machines but mayalso occur in infrastructure that supports the machine such as itswiring and local installation platforms. In embodiments, the largeindustrial machines may face different types of fault conditions such asoverheating, noise, grinding gears, excessive vibration of machineparts, fan vibration problems, problems with large industrial machinesrotating parts.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromindustrial machinery including failures that may be caused by prematurebearing failure that may occur due to contamination or loss of bearinglubricant. In another example, a mechanical defect such as misalignmentof bearings may occur. Many factors may contribute to the failure suchas metal fatigue, therefore, the local data collection system 102 maymonitor cycles and local stresses. By way of this example, the platform100 may monitor the incorrect operation of machine parts, lack ofmaintenance and servicing of parts, corrosion of vital machine parts,such as couplings or gearboxes, misalignment of machine parts, and thelike. Though the fault occurrences cannot be completely stopped, manyindustrial breakdowns may be mitigated to reduce operational andfinancial losses. The platform 100 provides real-time monitoring andpredictive maintenance in many industrial environments where it has beenshown to present a cost-savings over regularly-scheduled maintenanceprocesses that replace parts according to a rigid expiration of time andnot actual load and wear and tear on the element or machine. To thatend, the platform 10 may provide reminders of, or perform some,preventive measures such as adhering to operating manual and modeinstructions for machines, proper lubrication, and maintenance ofmachine parts, minimizing or eliminating overrun of machines beyondtheir defined capacities, replacement of worn but still functional partsas needed, properly training the personnel for machine use, and thelike.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor multiple signalsthat may be carried by a plurality of physical, electronic, and symbolicformats or signals. The platform 100 may employ signal processingincluding a plurality of mathematical, statistical, computational,heuristic, and linguistic representations and processing of signals anda plurality of operations needed for extraction of useful informationfrom signal processing operations such as techniques for representation,modeling, analysis, synthesis, sensing, acquisition, and extraction ofinformation from signals. In examples, signal processing may beperformed using a plurality of techniques, including but not limited totransformations, spectral estimations, statistical operations,probabilistic and stochastic operations, numerical theory analysis, datamining, and the like. The processing of various types of signals formsthe basis of many electrical or computational process. As a result,signal processing applies to almost all disciplines and applications inthe industrial environment such as audio and video processing, imageprocessing, wireless communications, process control, industrialautomation, financial systems, feature extraction, quality improvementssuch as noise reduction, image enhancement, and the like. Signalprocessing for images may include pattern recognition for manufacturinginspections, quality inspection, and automated operational inspectionand maintenance. The platform 100 may employ many pattern recognitiontechniques including those that may classify input data into classesbased on key features with the objective of recognizing patterns orregularities in data. The platform 100 may also implement patternrecognition processes with machine learning operations and may be usedin applications such as computer vision, speech and text processing,radar processing, handwriting recognition, CAD systems, and the like.The platform 100 may employ supervised classification and unsupervisedclassification. The supervised learning classification algorithms may bebased to create classifiers for image or pattern recognition, based ontraining data obtained from different object classes. The unsupervisedlearning classification algorithms may operate by finding hiddenstructures in unlabeled data using advanced analysis techniques such assegmentation and clustering. For example, some of the analysistechniques used in unsupervised learning may include K-means clustering,Gaussian mixture models, Hidden Markov models, and the like. Thealgorithms used in supervised and unsupervised learning methods ofpattern recognition enable the use of pattern recognition in varioushigh precision applications. The platform 100 may use patternrecognition in face detection related applications such as securitysystems, tracking, sports related applications, fingerprint analysis,medical and forensic applications, navigation and guidance systems,vehicle tracking, public infrastructure systems such as transportsystems, license plate monitoring, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 using machine learning toenable derivation-based learning outcomes from computers without theneed to program them. The platform 100 may, therefore, learn from andmake decisions on a set of data, by making data-driven predictions andadapting according to the set of data. In embodiments, machine learningmay involve performing a plurality of machine learning tasks by machinelearning systems, such as supervised learning, unsupervised learning,and reinforcement learning. Supervised learning may include presenting aset of example inputs and desired outputs to the machine learningsystems. Unsupervised learning may include the learning algorithm itselfstructuring its input by methods such as pattern detection and/orfeature learning. Reinforcement learning may include the machinelearning systems performing in a dynamic environment and then providingfeedback about correct and incorrect decisions. In examples, machinelearning may include a plurality of other tasks based on an output ofthe machine learning system. In examples, the tasks may also beclassified as machine learning problems such as classification,regression, clustering, density estimation, dimensionality reduction,anomaly detection, and the like. In examples, machine learning mayinclude a plurality of mathematical and statistical techniques. Inexamples, the many types of machine learning algorithms may includedecision tree based learning, association rule learning, deep learning,artificial neural networks, genetic learning algorithms, inductive logicprogramming, support vector machines (SVMs), Bayesian network,reinforcement learning, representation learning, rule-based machinelearning, sparse dictionary learning, similarity and metric learning,learning classifier systems (LCS), logistic regression, random forest,K-Means, gradient boost and adaboost, K-nearest neighbors (KNN), apriori algorithms, and the like. In embodiments, certain machinelearning algorithms may be used (such as genetic algorithms defined forsolving both constrained and unconstrained optimization problems thatmay be based on natural selection, the process that drives biologicalevolution). By way of this example, genetic algorithms may be deployedto solve a variety of optimization problems that are not well suited forstandard optimization algorithms, including problems in which theobjective functions are discontinuous, not differentiable, stochastic,or highly nonlinear. In an example, the genetic algorithm may be used toaddress problems of mixed integer programming, where some componentsrestricted to being integer-valued. Genetic algorithms and machinelearning techniques and systems may be used in computationalintelligence systems, computer vision, Natural Language Processing(NLP), recommender systems, reinforcement learning, building graphicalmodels, and the like. By way of this example, the machine learningsystems may be used to perform intelligent computing based control andbe responsive to tasks in a wide variety of systems (such as interactivewebsites and portals, brain-machine interfaces, online security andfraud detection systems, medical applications such as diagnosis andtherapy assistance systems, classification of DNA sequences, and thelike). In examples, machine learning systems may be used in advancedcomputing applications (such as online advertising, natural languageprocessing, robotics, search engines, software engineering, speech andhandwriting recognition, pattern matching, game playing, computationalanatomy, bioinformatics systems and the like). In an example, machinelearning may also be used in financial and marketing systems (such asfor user behavior analytics, online advertising, economic estimations,financial market analysis, and the like).

Additional details are provided below in connection with the methods,systems, devices, and components depicted in connection with FIGS. 1through 6. In embodiments, methods and systems are disclosed herein forcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. For example, data streams from vibration,pressure, temperature, accelerometer, magnetic, electrical field, andother analog sensors may be multiplexed or otherwise fused, relayed overa network, and fed into a cloud-based machine learning facility, whichmay employ one or more models relating to an operating characteristic ofan industrial machine, an industrial process, or a component or elementthereof. A model may be created by a human who has experience with theindustrial environment and may be associated with a training data set(such as models created by human analysis or machine analysis of datathat is collected by the sensors in the environment, or sensors in othersimilar environments. The learning machine may then operate on otherdata, initially using a set of rules or elements of a model, such as toprovide a variety of outputs, such as classification of data into types,recognition of certain patterns (such as those indicating the presenceof faults, orthoses indicating operating conditions, such as fuelefficiency, energy production, or the like). The machine learningfacility may take feedback, such as one or more inputs or measures ofsuccess, such that it may train, or improve, its initial model (such asimprovements by adjusting weights, rules, parameters, or the like, basedon the feedback). For example, a model of fuel consumption by anindustrial machine may include physical model parameters thatcharacterize weights, motion, resistance, momentum, inertia,acceleration, and other factors that indicate consumption, and chemicalmodel parameters (such as those that predict energy produced and/orconsumed e.g., such as through combustion, through chemical reactions inbattery charging and discharging, and the like). The model may berefined by feeding in data from sensors disposed in the environment of amachine, in the machine, and the like, as well as data indicating actualfuel consumption, so that the machine can provide increasingly accurate,sensor-based, estimates of fuel consumption and can also provide outputthat indicate what changes can be made to increase fuel consumption(such as changing operation parameters of the machine or changing otherelements of the environment, such as the ambient temperature, theoperation of a nearby machine, or the like). For example, if a resonanceeffect between two machines is adversely affecting one of them, themodel may account for this and automatically provide an output thatresults in changing the operation of one of the machines (such as toreduce the resonance, to increase fuel efficiency of one or bothmachines). By continuously adjusting parameters to cause outputs tomatch actual conditions, the machine learning facility may self-organizeto provide a highly accurate model of the conditions of an environment(such as for predicting faults, optimizing operational parameters, andthe like). This may be used to increase fuel efficiency, to reduce wear,to increase output, to increase operating life, to avoid faultconditions, and for many other purposes.

FIG. 14 illustrates components and interactions of a data collectionarchitecture involving the application of cognitive and machine learningsystems to data collection and processing. Referring to FIG. 14, thedata collection system 102 may be disposed in an environment (such as anindustrial environment where one or more complex systems, such aselectro-mechanical systems and machines are manufactured, assembled, oroperated). The data collection system 102 may include onboard sensorsand may take input, such as through one or more input interfaces orports 4008, from one or more sensors (such as analog or digital sensorsof any type disclosed herein) and from one or more input sources 116(such as sources that may be available through Wi-Fi, Bluetooth, NFC, orother local network connections or over the Internet). Sensors may becombined and multiplexed (such as with one or more multiplexers 4002).Data may be cached or buffered in a cache/buffer 4022 and made availableto external systems, such as a remote host processing system 112 asdescribed elsewhere in this disclosure (which may include an extensiveprocessing architecture 4024, including any of the elements described inconnection with other embodiments described throughout this disclosureand in the Figure), though one or more output interfaces and ports 4010(which may in embodiments be separate from or the same as the inputinterfaces and ports 4008). The data collection system 102 may beconfigured to take input from a host processing system 112, such asinput from an analytic system 4018, which may operate on data from thedata collection system 102 and data from other input sources 116 toprovide analytic results, which in turn may be provided as a learningfeedback input 4012 to the data collection system, such as to assist inconfiguration and operation of the data collection system 102.

Combination of inputs (including selection of what sensors or inputsources to turn “on” or “off”) may be performed under the control ofmachine-based intelligence, such as using a local cognitive inputselection system 4004, an optionally remote cognitive input selectionsystem 4114, or a combination of the two. The cognitive input selectionsystems 4004, 4014 may use intelligence and machine learningcapabilities described elsewhere in this disclosure, such as usingdetected conditions (such as conditions informed by the input sources116 or sensors), state information (including state informationdetermined by a machine state recognition system 4020 that may determinea state), such as relating to an operational state, an environmentalstate, a state within a known process or workflow, a state involving afault or diagnostic condition, or many others. This may includeoptimization of input selection and configuration based on learningfeedback from the learning feedback system 4012, which may includeproviding training data (such as from the host processing system 112 orfrom other data collection systems 102 either directly or from the hostprocessing system 112) and may include providing feedback metrics, suchas success metrics calculated within the analytic system 4018 of thehost processing system 112. For example, if a data stream consisting ofa particular combination of sensors and inputs yields positive resultsin a given set of conditions (such as providing improved patternrecognition, improved prediction, improved diagnosis, improved yield,improved return on investment, improved efficiency, or the like), thenmetrics relating to such results from the analytic system 4018 can beprovided via the learning feedback system 4012 to the cognitive inputselection systems 4004, 4014 to help configure future data collection toselect that combination in those conditions (allowing other inputsources to be de-selected, such as by powering down the other sensors).In embodiments, selection and de-selection of sensor combinations, undercontrol of one or more of the cognitive input selection systems 4004,may occur with automated variation, such as using genetic programmingtechniques, based on the learning feedback 4012, such as from theanalytic system 4018, effective combinations for a given state or set ofconditions are promoted, and less effective combinations are demoted,resulting in progressive optimization and adaptation of the local datacollection system to each unique environment. Thus, an automaticallyadapting, multi-sensor data collection system is provided, wherecognitive input selection is used (with feedback) to improve theeffectiveness, efficiency, or other performance parameters of the datacollection system within its particular environment. Performanceparameters may relate to overall system metrics (such as financialyields, process optimization results, energy production or usage, andthe like), analytic metrics (such as success in recognizing patterns,making predictions, classifying data, or the like), and local systemmetrics (such as bandwidth utilization, storage utilization, powerconsumption, and the like). In embodiments, the analytic system 4018,the state system 4020 and the cognitive input selection system 4114 of ahost may take data from multiple data collection systems 102, such thatoptimization (including of input selection) may be undertaken throughcoordinated operation of multiple systems 102. For example, thecognitive input selection system 4114 may understand that if one datacollection system 102 is already collecting vibration data for anX-axis, the X-axis vibration sensor for the other data collection systemmight be turned off, in favor of getting Y-axis data from the other datacollector 102. Thus, through coordinated collection by the hostcognitive input selection system 4114, the activity of multiplecollectors 102, across a host of different sensors, can provide for arich data set for the host processing system 112, without wastingenergy, bandwidth, storage space, or the like. As noted above,optimization may be based on overall system success metrics, analyticsuccess metrics, and local system metrics, or a combination of theabove.

Methods and systems are disclosed herein for cloud-based, machinepattern analysis of state information from multiple industrial sensorsto provide anticipated state information for an industrial system. Inembodiments, machine learning may take advantage of a state machine,such as tracking states of multiple analog and/or digital sensors,feeding the states into a pattern analysis facility, and determininganticipated states of the industrial system based on historical dataabout sequences of state information. For example, where a temperaturestate of an industrial machine exceeds a certain threshold and isfollowed by a fault condition, such as breaking down of a set ofbearings, that temperature state may be tracked by a pattern recognizer,which may produce an output data structure indicating an anticipatedbearing fault state (whenever an input state of a high temperature isrecognized). A wide range of measurement values and anticipated statesmay be managed by a state machine, relating to temperature, pressure,vibration, acceleration, momentum, inertia, friction, heat, heat flux,galvanic states, magnetic field states, electrical field states,capacitance states, charge and discharge states, motion, position, andmany others. States may comprise combined states, where a data structureincludes a series of states, each of which is represented by a place ina byte-like data structure. For example, an industrial machine may becharacterized by a genetic structure, such as one that providespressure, temperature, vibration, and acoustic data, the measurement ofwhich takes one place in the data structure, so that the combined statecan be operated on as a byte-like structure, such as a structure forcompactly characterizing the current combined state of the machine orenvironment, or compactly characterizing the anticipated state. Thisbyte-like structure can be used by a state machine for machine learning,such as pattern recognition that operates on the structure to determinepatterns that reflect combined effects of multiple conditions. A widevariety of such structure can be tracked and used, such as in machinelearning, representing various combinations, of various length, of thedifferent elements that can be sensed in an industrial environment. Inembodiments, byte-like structures can be used in a genetic programmingtechnique, such as by substituting different types of data, or data fromvarying sources, and tracking outcomes over time, so that one or morefavorable structures emerges based on the success of those structureswhen used in real world situations, such as indicating successfulpredictions of anticipated states, or achievement of success operationaloutcomes, such as increased efficiency, successful routing ofinformation, achieving increased profits, or the like. That is, byvarying what data types and sources are used in byte-like structuresthat are used for machine optimization over time, a geneticprogramming-based machine learning facility can “evolve” a set of datastructures, consisting of a favorable mix of data types (e.g., pressure,temperature, and vibration), from a favorable mix of data sources (e.g.,temperature is derived from sensor X, while vibration comes from sensorY), for a given purpose. Different desired outcomes may result indifferent data structures that are best adapted to support effectiveachievement of those outcomes over time with application of machinelearning and promotion of structures with favorable results for thedesired outcome in question by genetic programming. The promoted datastructures may provide compact, efficient data for various activities asdescribed throughout this disclosure, including being stored in datapools (which may be optimized by storing favorable data structures thatprovide the best operational results for a given environment), beingpresented in data marketplaces (such as being presented as the mosteffective structures for a given purpose), and the like.

In embodiments, a platform is provided having cloud-based, machinepattern analysis of state information from multiple analog industrialsensors to provide anticipated state information for an industrialsystem. In embodiments, the host processing system 112, such as disposedin the cloud, may include the state system 4020, which may be used toinfer or calculate a current state or to determine an anticipated futurestate relating to the data collection system 102 or some aspect of theenvironment in which the data collection system 102 is disposed, such asthe state of a machine, a component, a workflow, a process, an event(e.g., whether the event has occurred), an object, a person, acondition, a function, or the like. Maintaining state information allowsthe host processing system 112 to undertake analysis, such as in one ormore analytic systems 4018, to determine contextual information, toapply semantic and conditional logic, and perform many other functionsas enabled by the processing architecture 4024 described throughout thisdisclosure.

In embodiments, a platform is provided having cloud-based policyautomation engine for IoT, with creation, deployment, and management ofIoT devices. In embodiments, the platform 100 includes (or is integratedwith, or included in) the host processing system 112, such as on a cloudplatform, a policy automation engine 4032 for automating creation,deployment, and management of policies to IoT devices. Polices, whichmay include access policies, network usage policies, storage usagepolicies, bandwidth usage policies, device connection policies, securitypolicies, rule-based policies, role-based polices, and others, may berequired to govern the use of IoT devices. For example, as IoT devicesmay have many different network and data communications to otherdevices, policies may be needed to indicate to what devices a givendevice can connect, what data can be passed on, and what data can bereceived. As billions of devices with countless potential connectionsare expected to be deployed in the near future, it becomes impossiblefor humans to configure policies for IoT devices on aconnection-by-connection basis. Accordingly, the intelligent policyautomation engine 4032 may include cognitive features for creating,configuring, and managing policies. The policy automation engine 4032may consume information about possible policies, such as from a policydatabase or library, which may include one or more public sources ofavailable policies. These may be written in one or more conventionalpolicy languages or scripts. The policy automation engine 4032 may applythe policies according to one or more models, such as based on thecharacteristics of a given device, machine, or environment. For example,a large machine, such as a machine for power generation, may include apolicy that only a verifiably local controller can change certainparameters of the power generation, thereby avoiding a remote “takeover”by a hacker. This may be accomplished in turn by automatically findingand applying security policies that bar connection of the controlinfrastructure of the machine to the Internet, by requiring accessauthentication, or the like. The policy automation engine 4032 mayinclude cognitive features, such as varying the application of policies,the configuration of policies, and the like (such as features based onstate information from the state system 4020). The policy automationengine 4032 may take feedback, as from the learning feedback system4012, such as based on one or more analytic results from the analyticsystem 4018, such as based on overall system results (such as the extentof security breaches, policy violations, and the like), local results,and analytic results. By variation and selection based on such feedback,the policy automation engine 4032 can, over time, learn to automaticallycreate, deploy, configure, and manage policies across very large numbersof devices, such as managing policies for configuration of connectionsamong IoT devices.

Methods and systems are disclosed herein for on-device sensor fusion anddata storage for industrial IoT devices, including on-device sensorfusion and data storage for an industrial IoT device, where data frommultiple sensors is multiplexed at the device for storage of a fuseddata stream. For example, pressure and temperature data may bemultiplexed into a data stream that combines pressure and temperature ina time series, such as in a byte-like structure (where time, pressure,and temperature are bytes in a data structure, so that pressure andtemperature remain linked in time, without requiring separate processingof the streams by outside systems), or by adding, dividing, multiplying,subtracting, or the like, such that the fused data can be stored on thedevice. Any of the sensor data types described throughout thisdisclosure can be fused in this manner and stored in a local data pool,in storage, or on an IoT device, such as a data collector, a componentof a machine, or the like.

In embodiments, a platform is provided having on-device sensor fusionand data storage for industrial IoT devices. In embodiments, a cognitivesystem is used for a self-organizing storage system 4028 for the datacollection system 102. Sensor data, and in particular analog sensordata, can consume large amounts of storage capacity, in particular wherea data collector 102 has multiple sensor inputs onboard or from thelocal environment. Simply storing all the data indefinitely is nottypically a favorable option, and even transmitting all of the data maystrain bandwidth limitations, exceed bandwidth permissions (such asexceeding cellular data plan capacity), or the like. Accordingly,storage strategies are needed. These typically include capturing onlyportions of the data (such as snapshots), storing data for limited timeperiods, storing portions of the data (such as intermediate orabstracted forms), and the like. With many possible selections amongthese and other options, determining the correct storage strategy may behighly complex. In embodiments, the self-organizing storage system 4028may use a cognitive system, based on the learning feedback 4012, and usevarious metrics from the analytic system 4018 or other system of thehost cognitive input selection system 4114, such as overall systemmetrics, analytic metrics, and local performance indicators. Theself-organizing storage system 4028 may automatically vary storageparameters, such as storage locations (including local storage on thedata collection system 102, storage on nearby data collection systems102 (such as using peer-to-peer organization) and remote storage, suchas network-based storage), storage amounts, storage duration, type ofdata stored (including individual sensors or input sources 116, as wellas various combined or multiplexed data, such as selected under thecognitive input selection systems 4004, 4014), storage type (such asusing RAM, Flash, or other short-term memory versus available hard drivespace), storage organization (such as in raw form, in hierarchies, andthe like), and others. Variation of the parameters may be undertakenwith feedback, so that over time the data collection system 102 adaptsits storage of data to optimize itself to the conditions of itsenvironment, such as a particular industrial environment, in a way thatresults in it storing the data that is needed in the right amounts andof the right type for availability to users.

In embodiments, the local cognitive input selection system 4004 mayorganize fusion of data for various onboard sensors, external sensors(such as in the local environment) and other input sources 116 to thelocal data collection system 102 into one or more fused data streams,such as using the multiplexer 4002 to create various signals thatrepresent combinations, permutations, mixes, layers, abstractions,data-metadata combinations, and the like of the source analog and/ordigital data that is handled by the data collection system 102. Theselection of a particular fusion of sensors may be determined locally bythe cognitive input selection system 4004, such as based on learningfeedback from the learning feedback system 4012, such as various overallsystem, analytic system and local system results and metrics. Inembodiments, the system may learn to fuse particular combinations andpermutations of sensors, such as in order to best achieve correctanticipation of state, as indicated by feedback of the analytic system4018 regarding its ability to predict future states, such as the variousstates handled by the state system 4020. For example, the inputselection system 4004 may indicate selection of a sub-set of sensorsamong a larger set of available sensors, and the inputs from theselected sensors may be combined, such as by placing input from each ofthem into a byte of a defined, multi-bit data structure (such as acombination by taking a signal from each at a given sampling rate ortime and placing the result into the byte structure, then collecting andprocessing the bytes over time), by multiplexing in the multiplexer4002, such as a combination by additive mixing of continuous signals,and the like. Any of a wide range of signal processing and dataprocessing techniques for combination and fusing may be used, includingconvolutional techniques, coercion techniques, transformationtechniques, and the like. The particular fusion in question may beadapted to a given situation by cognitive learning, such as by havingthe cognitive input selection system 4004 learn, based on the feedback4012 from results (such as feedback conveyed by the analytic system4018), such that the local data collection system 102 executescontext-adaptive sensor fusion.

In embodiments, the analytic system 4018 may apply to any of a widerange of analytic techniques, including statistical and econometrictechniques (such as linear regression analysis, use similarity matrices,heat map based techniques, and the like), reasoning techniques (such asBayesian reasoning, rule-based reasoning, inductive reasoning, and thelike), iterative techniques (such as feedback, recursion, feed-forwardand other techniques), signal processing techniques (such as Fourier andother transforms), pattern recognition techniques (such as Kalman andother filtering techniques), search techniques, probabilistic techniques(such as random walks, random forest algorithms, and the like),simulation techniques (such as random walks, random forest algorithms,linear optimization and the like), and others. This may includecomputation of various statistics or measures. In embodiments, theanalytic system 4018 may be disposed, at least in part, on the datacollection system 102, such that a local analytic system can calculateone or more measures, such as measures relating to any of the itemsnoted throughout this disclosure. For example, measures of efficiency,power utilization, storage utilization, redundancy, entropy, and otherfactors may be calculated onboard, so that the data collection 102 canenable various cognitive and learning functions noted throughout thisdisclosure without dependence on a remote (e.g., cloud-based) analyticsystem.

In embodiments, the host processing system 112, the data collectionsystem 102, or both, may include, connect to, or integrate with, theself-organizing networking system 4020, which may comprise a cognitivesystem for providing machine-based, intelligent or organization ofnetwork utilization for transport of data in a data collection system,such as for handling analog and other sensor data, or other source data,such as among one or more local data collection systems 102 and a hostprocessing system 112. This may include organizing network utilizationfor source data delivered to data collection systems, for feedback data,such as analytic data provided to or via the learning feedback system4012, data for supporting a marketplace (such as described in connectionwith other embodiments), and output data provided via output interfacesand ports 4010 from one or more data collection systems 102.

Methods and systems are disclosed herein for a self-organizing datamarketplace for industrial IoT data, including where available dataelements are organized in the marketplace for consumption by consumersbased on training a self-organizing facility with a training set andfeedback from measures of marketplace success. A marketplace may be setup initially to make available data collected from one or moreindustrial environments, such as presenting data by type, by source, byenvironment, by machine, by one or more patterns, or the like (such asin a menu or hierarchy). The marketplace may vary the data collected,the organization of the data, the presentation of the data (includingpushing the data to external sites, providing links, configuring APIs bywhich the data may be accessed, and the like), the pricing of the data,or the like, such as under machine learning, which may vary differentparameters of any of the foregoing. The machine learning facility maymanage all of these parameters by self-organization, such as by varyingparameters over time (including by varying elements of the data typespresented), the data sourced used to obtain each type of data, the datastructures presented (such as byte-like structures, fused or multiplexedstructures (such as representing multiple sensor types), and statisticalstructures (such as representing various mathematical products of sensorinformation), among others), the pricing for the data, where the data ispresented, how the data is presented (such as by APIs, by links, by pushmessaging, and the like), how the data is stored, how the data isobtained, and the like. As parameters are varied, feedback may beobtained as to measures of success, such as number of views, yield(e.g., price paid) per access, total yield, per unit profit, aggregateprofit, and many others, and the self-organizing machine learningfacility may promote configurations that improve measures of success anddemote configurations that do not, so that, over time, the marketplaceis progressively configured to present favorable combinations of datatypes (e.g., those that provide robust prediction of anticipated statesof particular industrial environments of a given type), from favorablesources (e.g., those that are reliable, accurate and low priced), witheffective pricing (e.g., pricing that tends to provide high aggregateprofit from the marketplace). The marketplace may include spiders, webcrawlers, and the like to seek input data sources, such as finding datapools, connected IoT devices, and the like that publish potentiallyrelevant data. These may be trained by human users and improved bymachine learning in a manner similar to that described elsewhere in thisdisclosure.

In embodiments, a platform is provided having a self-organizing datamarketplace for industrial IoT data. Referring to FIG. 15, inembodiments, a platform is provided having a cognitive data marketplace4102, referred to in some cases as a self-organizing data marketplace,for data collected by one or more data collection systems 102 or fordata from other sensors or input sources 116 that are located in variousdata collection environments, such as industrial environments. Inaddition to data collection systems 102, this may include datacollected, handled or exchanged by IoT devices, such as cameras,monitors, embedded sensors, mobile devices, diagnostic devices andsystems, instrumentation systems, telematics systems, and the like, suchas for monitoring various parameters and features of machines, devices,components, parts, operations, functions, conditions, states, events,workflows and other elements (collectively encompassed by the term“states”) of such environments. Data may also include metadata about anyof the foregoing, such as describing data, indicating provenance,indicating elements relating to identity, access, roles, andpermissions, providing summaries or abstractions of data, or otherwiseaugmenting one or more items of data to enable further processing, suchas for extraction, transforming, loading, and processing data. Such data(such term including metadata except where context indicates otherwise)may be highly valuable to third parties, either as an individual element(such as the instance where data about the state of an environment canbe used as a condition within a process) or in the aggregate (such asthe instance where collected data, optionally over many systems anddevices in different environments can be used to develop models ofbehavior, to train learning systems, or the like). As billions of IoTdevices are deployed, with countless connections, the amount ofavailable data will proliferate. To enable access and utilization ofdata, the cognitive data marketplace 4102 enables various components,features, services, and processes for enabling users to supply, find,consume, and transact in packages of data, such as batches of data,streams of data (including event streams), data from various data pools4120, and the like. In embodiments, the cognitive data marketplace 4102may be included in, connected to, or integrated with, one or more othercomponents of the host processing architecture 4024 of the hostprocessing system 112, such as a cloud-based system, as well as tovarious sensors, input sources 115, data collection systems 102 and thelike. The cognitive data marketplace 4102 may include marketplaceinterfaces 4108, which may include one or more supplier interfaces bywhich data suppliers may make data available and one more consumerinterfaces by which data may be found and acquired. The consumerinterface may include an interface to a data market search system 4118,which may include features that enable a user to indicate what types ofdata a user wishes to obtain, such as by entering keywords in a naturallanguage search interface that characterize data or metadata. The searchinterface can use various search and filtering techniques, includingkeyword matching, collaborative filtering (such as using knownpreferences or characteristics of the consumer to match to similarconsumers and the past outcomes of those other consumers), rankingtechniques (such as ranking based on success of past outcomes accordingto various metrics, such as those described in connection with otherembodiments in this disclosure). In embodiments, a supply interface mayallow an owner or supplier of data to supply the data in one or morepackages to and through the cognitive data marketplace 4102, such aspackaging batches of data, streams of data, or the like. The suppliermay pre-package data, such as by providing data from a single inputsource 116, a single sensor, and the like, or by providing combinations,permutations, and the like (such as multiplexed analog data, mixed bytesof data from multiple sources, results of extraction, loading andtransformation, results of convolution, and the like), as well as byproviding metadata with respect to any of the foregoing. Packaging mayinclude pricing, such as on a per-batch basis, on a streaming basis(such as subscription to an event feed or other feed or stream), on aper item basis, on a revenue share basis, or other basis. For datainvolving pricing, a data transaction system 4114 may track orders,delivery, and utilization, including fulfillment of orders. Thetransaction system 4114 may include rich transaction features, includingdigital rights management, such as by managing cryptographic keys thatgovern access control to purchased data, that govern usage (such asallowing data to be used for a limited time, in a limited domain, by alimited set of users or roles, or for a limited purpose). Thetransaction system 4114 may manage payments, such as by processingcredit cards, wire transfers, debits, and other forms of consideration.

In embodiments, a cognitive data packaging system 4010 of themarketplace 4102 may use machine-based intelligence to package data,such as by automatically configuring packages of data in batches,streams, pools, or the like. In embodiments, packaging may be accordingto one or more rules, models, or parameters, such as by packaging oraggregating data that is likely to supplement or complement an existingmodel. For example, operating data from a group of similar machines(such as one or more industrial machines noted throughout thisdisclosure) may be aggregated together, such as based on metadataindicating the type of data or by recognizing features orcharacteristics in the data stream that indicate the nature of the data.In embodiments, packaging may occur using machine learning and cognitivecapabilities, such as by learning what combinations, permutations,mixes, layers, and the like of input sources 116, sensors, informationfrom data pools 4120 and information from data collection systems 102are likely to satisfy user requirements or result in measures ofsuccess. Learning may be based on the learning feedback 4012, such aslearning based on measures determined in the analytic system 4018, suchas system performance measures, data collection measures, analyticmeasures, and the like. In embodiments, success measures may becorrelated to marketplace success measures, such as viewing of packages,engagement with packages, purchase or licensing of packages, paymentsmade for packages, and the like. Such measures may be calculated in theanalytic system 4018, including associating particular feedback measureswith search terms and other inputs, so that a cognitive packaging system4110 can find and configure packages that are designed to provideincreased value to consumers and increased returns for data suppliers.In embodiments, the cognitive data packaging system 4110 canautomatically vary packaging, such as using different combinations,permutations, mixes, and the like, and varying weights applied to giveninput sources, sensors, data pools and the like, using the learningfeedback 4012 to promote favorable packages and de-emphasize lessfavorable packages. This may occur using genetic programming and similartechniques that compare outcomes for different packages. Feedback mayinclude state information from the state system 4020 (such as aboutvarious operating states, and the like), as well as about marketplaceconditions and states, such as pricing and availability information forother data sources. Thus, an adaptive cognitive data packaging system4110 is provided that automatically adapts to conditions to providefavorable packages of data for the marketplace 4102.

In embodiments, a cognitive data pricing system 4112 may be provided toset pricing for data packages. In embodiments, the data pricing system4112 may use a set of rules, models, or the like, such as settingpricing based on supply conditions, demand conditions, pricing ofvarious available sources, and the like. For example, pricing for apackage may be configured to be set based on the sum of the prices ofconstituent elements (such as input sources, sensor data, or the like),or to be set based on a rule-based discount to the sum of prices forconstituent elements, or the like. Rules and conditional logic may beapplied, such as rules that factor in cost factors (such as bandwidthand network usage, peak demand factors, scarcity factors, and the like),rules that factor in utilization parameters (such as the purpose,domain, user, role, duration, or the like for a package) and manyothers. In embodiments, the cognitive data pricing system 4112 mayinclude fully cognitive, intelligent features, such as using geneticprogramming including automatically varying pricing and trackingfeedback on outcomes. Outcomes on which tracking feedback may be basedinclude various financial yield metrics, utilization metrics and thelike that may be provided by calculating metrics in the analytic system4018 on data from the data transaction system 4114.

Methods and systems are disclosed herein for self-organizing data poolswhich may include self-organization of data pools based on utilizationand/or yield metrics, including utilization and/or yield metrics thatare tracked for a plurality of data pools. The data pools may initiallycomprise unstructured or loosely structured pools of data that containdata from industrial environments, such as sensor data from or aboutindustrial machines or components. For example, a data pool might takestreams of data from various machines or components in an environment,such as turbines, compressors, batteries, reactors, engines, motors,vehicles, pumps, rotors, axles, bearings, valves, and many others, withthe data streams containing analog and/or digital sensor data (of a widerange of types), data published about operating conditions, diagnosticand fault data, identifying data for machines or components, assettracking data, and many other types of data. Each stream may have anidentifier in the pool, such as indicating its source, and optionallyits type. The data pool may be accessed by external systems, such asthrough one or more interfaces or APIs (e.g., RESTful APIs), or by dataintegration elements (such as gateways, brokers, bridges, connectors, orthe like), and the data pool may use similar capabilities to get accessto available data streams. A data pool may be managed by aself-organizing machine learning facility, which may configure the datapool, such as by managing what sources are used for the pool, managingwhat streams are available, and managing APIs or other connections intoand out of the data pool. The self-organization may take feedback suchas based on measures of success that may include measures of utilizationand yield. The measures of utilization and yield that may include mayaccount for the cost of acquiring and/or storing data, as well as thebenefits of the pool, measured either by profit or by other measuresthat may include user indications of usefulness, and the like. Forexample, a self-organizing data pool might recognize that chemical andradiation data for an energy production environment are regularlyaccessed and extracted, while vibration and temperature data have notbeen used, in which case the data pool might automatically reorganize,such as by ceasing storage of vibration and/or temperature data, or byobtaining better sources of such data. This automated reorganization canalso apply to data structures, such as promoting different data types,different data sources, different data structures, and the like, throughprogressive iteration and feedback.

In embodiments, a platform is provided having self-organization of datapools based on utilization and/or yield metrics. In embodiments, thedata pools 4120 may be self-organizing data pools 4120, such as beingorganized by cognitive capabilities as described throughout thisdisclosure. The data pools 4120 may self-organize in response to thelearning feedback 4012, such as based on feedback of measures andresults, including calculated in the analytic system 4018.

Organization may include determining what data or packages of data tostore in a pool (such as representing particular combinations,permutations, aggregations, and the like), the structure of such data(such as in flat, hierarchical, linked, or other structures), theduration of storage, the nature of storage media (such as hard disks,flash memory, SSDs, network-based storage, or the like), the arrangementof storage bits, and other parameters. The content and nature of storagemay be varied, such that a data pool 4120 may learn and adapt, such asbased on states of the host processing system 112, one or more datacollection systems 102, storage environment parameters (such ascapacity, cost, and performance factors), data collection environmentparameters, marketplace parameters, and many others. In embodiments,pools 4120 may learn and adapt, such as by variation of the above andother parameters in response to yield metrics (such as return oninvestment, optimization of power utilization, optimization of revenue,and the like).

Methods and systems are disclosed herein for training AI models based onindustry-specific feedback, including training an AI model based onindustry-specific feedback that reflects a measure of utilization,yield, or impact, and where the AI model operates on sensor data from anindustrial environment. As noted above, these models may includeoperating models for industrial environments, machines, workflows,models for anticipating states, models for predicting fault andoptimizing maintenance, models for self-organizing storage (on devices,in data pools and/or in the cloud), models for optimizing data transport(such as for optimizing network coding, network-condition-sensitiverouting, and the like), models for optimizing data marketplaces, andmany others.

In embodiments, a platform is provided having training AI models basedon industry-specific feedback. In embodiments, the various embodimentsof cognitive systems disclosed herein may take inputs and feedback fromindustry-specific and domain-specific input sources 116 (such asrelating to optimization of specific machines, devices, components,processes, and the like). Thus, learning and adaptation of storageorganization, network usage, combination of sensor and input data, datapooling, data packaging, data pricing, and other features (such as forthe marketplace 4102 or for other purposes of the host processing system112) may be configured by learning on the domain-specific feedbackmeasures of a given environment or application, such as an applicationinvolving IoT devices (such as an industrial environment). This mayinclude optimization of efficiency (such as in electrical,electromechanical, magnetic, physical, thermodynamic, chemical and otherprocesses and systems), optimization of outputs (such as for productionof energy, materials, products, services and other outputs), prediction,avoidance and mitigation of faults (such as in the aforementionedsystems and processes), optimization of performance measures (such asreturns on investment, yields, profits, margins, revenues and the like),reduction of costs (including labor costs, bandwidth costs, data costs,material input costs, licensing costs, and many others), optimization ofbenefits (such as relating to safety, satisfaction, health),optimization of work flows (such as optimizing time and resourceallocation to processes), and others.

Methods and systems are disclosed herein for a self-organized swarm ofindustrial data collectors, including a self-organizing swarm ofindustrial data collectors that organize among themselves to optimizedata collection based on the capabilities and conditions of the membersof the swarm. Each member of the swarm may be configured withintelligence, and the ability to coordinate with other members. Forexample, a member of the swarm may track information about what dataother members are handling, so that data collection activities, datastorage, data processing, and data publishing can be allocatedintelligently across the swarm, taking into account conditions of theenvironment, capabilities of the members of the swarm, operatingparameters, rules (such as from a rules engine that governs theoperation of the swarm), and current conditions of the members. Forexample, among four collectors, one that has relatively low currentpower levels (such as a low battery), might be temporarily allocated therole of publishing data, because it may receive a dose of power from areader or interrogation device (such as an RFID reader) when it needs topublish the data. A second collector with good power levels and robustprocessing capability might be assigned more complex functions, such asprocessing data, fusing data, organizing the rest of the swarm(including self-organization under machine learning, such that the swarmis optimized over time, including by adjusting operating parameters,rules, and the like based on feedback), and the like. A third collectorin the swarm with robust storage capabilities might be assigned the taskof collecting and storing a category of data, such as vibration sensordata, that consumes considerable bandwidth. A fourth collector in theswarm, such as one with lower storage capabilities, might be assignedthe role of collecting data that can usually be discarded, such as dataon current diagnostic conditions, where only data on faults needs to bemaintained and passed along. Members of a swarm may connect bypeer-to-peer relationships by using a member as a “master” or “hub,” orby having them connect in a series or ring, where each member passesalong data (including commands) to the next, and is aware of the natureof the capabilities and commands that are suitable for the precedingand/or next member. The swarm may be used for allocation of storageacross it (such as using memory of each memory as an aggregate datastore. In these examples, the aggregate data store may support adistributed ledger, which may store transaction data, such as fortransactions involving data collected by the swarm, transactionsoccurring in the industrial environment, or the like. In embodiments,the transaction data may also include data used to manage the swarm, theenvironment, or a machine or components thereof. The swarm mayself-organize, either by machine learning capability disposed on one ormore members of the swarm, or based on instructions from an externalmachine learning facility, which may optimize storage, data collection,data processing, data presentation, data transport, and other functionsbased on managing parameters that are relevant to each. The machinelearning facility may start with an initial configuration and varyparameters of the swarm relevant to any of the foregoing (also includingvarying the membership of the swarm), such as iterating based onfeedback to the machine learning facility regarding measures of success(such as utilization measures, efficiency measures, measures of successin prediction or anticipation of states, productivity measures, yieldmeasures, profit measures, and others). Over time, the swarm may beoptimized to a favorable configuration to achieve the desired measure ofsuccess for an owner, operator, or host of an industrial environment ora machine, component, or process thereof.

A swarm 4202 may be organized based on a hierarchical organization (suchas where a master data collector 102 organizes and directs activities ofone or more subservient data collectors 102), a collaborativeorganization (such as where decision-making for the organization of theswarm 4202 is distributed among the data collectors 102 (such as usingvarious models for decision-making, such as voting systems, pointssystems, least-cost routing systems, prioritization systems, and thelike), and the like.) In embodiments, one or more of the data collectors102 may have mobility capabilities, such as in cases where a datacollector is disposed on or in a mobile robot, drone, mobilesubmersible, or the like, so that organization may include the locationand positioning of the data collectors 102. Data collection systems 102may communicate with each other and with the host processing system 112,including sharing an aggregate allocated storage space involving storageon or accessible to one or more of the collectors (which in embodimentmay be treated as a unified storage space even if physicallydistributed, such as using virtualization capabilities). Organizationmay be automated based on one or more rules, models, conditions,processes, or the like (such as embodied or executed by conditionallogic), and organization may be governed by policies, such as handled bythe policy engine. Rules may be based on industry, application- anddomain-specific objects, classes, events, workflows, processes, andsystems, such as by setting up the swarm 4202 to collect selected typesof data at designated places and times, such as coordinated with theforegoing. For example, the swarm 4202 may assign data collectors 102 toserially collect diagnostic, sensor, instrumentation and/or telematicdata from each of a series of machines that execute an industrialprocess (such as a robotic manufacturing process), such as at the timeand location of the input to and output from each of those machines. Inembodiments, self-organization may be cognitive, such as where the swarmvaries one or more collection parameters and adapts the selection ofparameters, weights applied to the parameters, or the like, over time.In examples, this may be in response to learning and feedback, such asfrom the learning feedback system 4012 that may be based on variousfeedback measures that may be determined by applying the analytic system4018 (which in embodiments may reside on the swarm 4202, the hostprocessing system 112, or a combination thereof) to data handled by theswarm 4202 or to other elements of the various embodiments disclosedherein (including marketplace elements and others). Thus, the swarm 4202may display adaptive behavior, such as adapting to the current state4020 or an anticipated state of its environment (accounting formarketplace behavior), behavior of various objects (such as IoT devices,machines, components, and systems), processes (including events, states,workflows, and the like), and other factors at a given time. Parametersthat may be varied in a process of variation (such as in a neural net,self-organizing map, or the like), selection, promotion, or the like(such as those enabled by genetic programming or other AI-basedtechniques). Parameters that may be managed, varied, selected andadapted by cognitive, machine learning may include storage parameters(location, type, duration, amount, structure and the like across theswarm 4202), network parameters (such as how the swarm 4202 isorganized, such as in mesh, peer-to-peer, ring, serial, hierarchical andother network configurations as well as bandwidth utilization, datarouting, network protocol selection, network coding type, and othernetworking parameters), security parameters (such as settings forvarious security applications and services), location and positioningparameters (such as routing movement of mobile data collectors 102 tolocations, positioning and orienting collectors 102 and the likerelative to points of data acquisition, relative to each other, andrelative to locations where network availability may be favorable, amongothers), input selection parameters (such as input selection amongsensors, input sources 116 and the like for each collector 102 and forthe aggregate collection), data combination parameters (such as thosefor sensor fusion, input combination, multiplexing, mixing, layering,convolution, and other combinations), power parameters (such asparameters based on power levels and power availability for one or morecollectors 102 or other objects, devices, or the like), states(including anticipated states and conditions of the swarm 4202,individual collection systems 102, the host processing system 112 or oneor more objects in an environment), events, and many others. Feedbackmay be based on any of the kinds of feedback described herein, such thatover time the swarm may adapt to its current and anticipated situationto achieve a wide range of desired objectives.

Methods and systems are disclosed herein for an industrial IoTdistributed ledger, including a distributed ledger supporting thetracking of transactions executed in an automated data marketplace forindustrial IoT data. A distributed ledger may distribute storage acrossdevices, using a secure protocol, such as those used forcryptocurrencies (such as the Blockchain™ protocol used to support theBitcoin™ currency). A ledger or similar transaction record, which maycomprise a structure where each successive member of a chain stores datafor previous transactions, and a competition can be established todetermine which of alternative data stored data structures is “best”(such as being most complete), can be stored across data collectors,industrial machines or components, data pools, data marketplaces, cloudcomputing elements, servers, and/or on the IT infrastructure of anenterprise (such as an owner, operator or host of an industrialenvironment or of the systems disclosed herein). The ledger ortransaction may be optimized by machine learning, such as to providestorage efficiency, security, redundancy, or the like.

In embodiments, the cognitive data marketplace 4102 may use a securearchitecture for tracking and resolving transactions, such as adistributed ledger 4004. In embodiments, transactions in data packagesare tracked in a chained, distributed data structure, such as aBlockchain™, allowing forensic analysis and validation where individualdevices store a portion of the ledger representing transactions in datapackages. The distributed ledger 4004 may be distributed to IoT devices,to data pools 4120, to data collection systems 102, and the like, sothat transaction information can be verified without reliance on asingle, central repository of information. The transaction system 4114may be configured to store data in the distributed ledger 4004 and toretrieve data from it (and from constituent devices) in order to resolvetransactions. Thus, a distributed ledger 4004 for handling transactionsin data, such as for packages of IoT data, is provided. In embodiments,the self-organizing storage system 4028 may be used for optimizingstorage of distributed ledger data, as well as for organizing storage ofpackages of data, such as IoT data, that can be presented in themarketplace 4102.

Methods and systems are disclosed herein for a network-sensitivecollector, including a network condition-sensitive, self-organizing,multi-sensor data collector that can optimize based on bandwidth,quality of service, pricing and/or other network conditions. Networksensitivity can include awareness of the price of data transport (suchas allowing the system to pull or push data during off-peak periods orwithin the available parameters of paid data plans), the quality of thenetwork (such as to avoid periods where errors are likely), the qualityof environmental conditions (such as delaying transmission until signalquality is good, such as when a collector emerges from a shieldedenvironment, avoiding wasting use of power when seeking a signal whenshielded, such as by large metal structures typically of industrialenvironments), and the like.

Methods and systems are disclosed herein for a remotely organizeduniversal data collector that can power up and down sensor interfacesbased on need and/or conditions identified in an industrial datacollection environment. For example, interfaces can recognize whatsensors are available and interfaces and/or processors can be turned onto take input from such sensors, including hardware interfaces thatallow the sensors to plug in to the data collector, wireless datainterfaces (such as where the collector can ping the sensor, optionallyproviding some power via an interrogation signal), and softwareinterfaces (such as for handling particular types of data). Thus, acollector that is capable of handling various kinds of data can beconfigured to adapt to the particular use in a given environment. Inembodiments, configuration may be automatic or under machine learning,which may improve configuration by optimizing parameters based onfeedback measures over time.

Methods and systems are disclosed herein for self-organizing storage fora multi-sensor data collector, including self-organizing storage for amulti-sensor data collector for industrial sensor data. Self-organizingstorage may allocate storage based on application of machine learning,which may improve storage configuration based on feedback measure overtime. Storage may be optimized by configuring what data types are used(e.g., byte-like structures, structures representing fused data frommultiple sensors, structures representing statistics or measurescalculated by applying mathematical functions on data, and the like), byconfiguring compression, by configuring data storage duration, byconfiguring write strategies (such as by striping data across multiplestorage devices, using protocols where one device stores instructionsfor other devices in a chain, and the like), and by configuring storagehierarchies, such as by providing pre-calculated intermediate statisticsto facilitate more rapid access to frequently accessed data items. Thus,highly intelligent storage systems may be configured and optimized,based on feedback, over time.

Methods and systems are disclosed herein for self-organizing networkcoding for a multi-sensor data network, including self-organizingnetwork coding for a data network that transports data from multiplesensors in an industrial data collection environment. Network coding,including random linear network coding, can enable highly efficient andreliable transport of large amounts of data over various kinds ofnetworks. Different network coding configurations can be selected, basedon machine learning, to optimize network coding and other networktransport characteristics based on network conditions, environmentalconditions, and other factors, such as the nature of the data beingtransported, environmental conditions, operating conditions, and thelike (including by training a network coding selection model over timebased on feedback of measures of success, such as any of the measuresdescribed herein).

In embodiments, a platform is provided having a self-organizing networkcoding for multi-sensor data network. A cognitive system may vary one ormore parameters for networking, such as network type selection (e.g.,selecting among available local, cellular, satellite, Wi-Fi, Bluetooth™,NFC, Zigbee® and other networks), network selection (such as selecting aspecific network, such as one that is known to have desired securityfeatures), network coding selection (such as selecting a type of networkcoding for efficient transport[such as random linear network coding,fixed coding, and others]), network timing selection (such asconfiguring delivery based on network pricing conditions, traffic andthe like), network feature selection (such as selecting cognitivefeatures, security features, and the like), network conditions (such asnetwork quality based on current environmental or operation conditions),network feature selection (such as enabling available authentication,permission and similar systems), network protocol selection (such asamong HTTP, IP, TCP/IP, cellular, satellite, serial, packet, streaming,and many other protocols), and others. Given bandwidth constraints,price variations, sensitivity to environmental factors, securityconcerns, and the like, selecting the optimal network configuration canbe highly complex and situation dependent. The self-organizingnetworking system 4030 may vary combinations and permutations of theseparameters while taking input from the learning feedback system 4012such as using information from the analytic system 4018 about variousmeasures of outcomes. In the many examples, outcomes may include overallsystem measures, analytic success measures, and local performanceindicators. In embodiments, input from the learning feedback system 4012may include information from various sensors and input sources 116,information from the state system 4020 about states (such as events,environmental conditions, operating conditions, and many others, orother information) or taking other inputs. By variation and selection ofalternative configurations of networking parameters in different states,the self-organizing networking system may find configurations that arewell-adapted to the environment that is being monitored or controlled bythe host processing system 112, such as the instance where one or moredata collection systems 102 are located and that are well-adapted toemerging network conditions. Thus, a self-organizing,network-condition-adaptive data collection system is provided.

Referring to FIG. 42, the data collection system 102 may have one ormore output interfaces and/or ports 4010. These may include networkports and connections, application programming interfaces, and the like.Methods and systems are disclosed herein for a haptic or multi-sensoryuser interface, including a wearable haptic or multi-sensory userinterface for an industrial sensor data collector, with vibration, heat,electrical, and/or sound outputs. For example, an interface may, basedon a data structure configured to support the interface, be set up toprovide a user with input or feedback, such as based on data fromsensors in the environment. For example, if a fault condition based on avibration data (such as resulting from a bearing being worn down, anaxle being misaligned, or a resonance condition between machines) isdetected, it can be presented in a haptic interface by vibration of aninterface, such as shaking a wrist-worn device. Similarly, thermal dataindicating overheating could be presented by warming or cooling awearable device, such as while a worker is working on a machine andcannot necessarily look at a user interface. Similarly, electrical ormagnetic data may be presented by a buzzing, and the like, such as toindicate presence of an open electrical connection or wire, etc. Thatis, a multi-sensory interface can intuitively help a user (such as auser with a wearable device) get a quick indication of what is going onin an environment, with the wearable interface having various modes ofinteraction that do not require a user to have eyes on a graphical UI,which may be difficult or impossible in many industrial environmentswhere a user needs to keep an eye on the environment.

In embodiments, a platform is provided having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical, and/or sound outputs. In embodiments, a haptic userinterface 4302 is provided as an output for the data collection system102, such as a system for handling and providing information forvibration, heat, electrical, and/or sound outputs, such as to one ormore components of the data collection system 102 or to another system,such as a wearable device, mobile phone, or the like. The datacollection system 102 may be provided in a form factor suitable fordelivering haptic input to a user, such as vibration, warming orcooling, buzzing, or the like, such as input disposed in headgear, anarmband, a wristband or watch, a belt, an item of clothing, a uniform,or the like. In such cases, data collection systems 102 may beintegrated with gear, uniforms, equipment, or the like worn by users,such as individuals responsible for operating or monitoring anindustrial environment. In embodiments, signals from various sensors orinput sources (or selective combinations, permutations, mixes, and thelike, as managed by one or more of the cognitive input selection systems4004, 4014) may trigger haptic feedback. For example, if a nearbyindustrial machine is overheating, the haptic interface may alert a userby warming up, or by sending a signal to another device (such as amobile phone) to warm up. If a system is experiencing unusualvibrations, the haptic interface may vibrate. Thus, through variousforms of haptic input, the data collection system 102 may inform usersof the need to attend to one or more devices, machines, or other factors(such as those in an industrial environment) without requiring them toread messages or divert their visual attention away from the task athand. The haptic interface, and selection of what outputs should beprovided, may be considered in the cognitive input selection systems4004, 4014. For example, user behavior (such as responses to inputs) maybe monitored and analyzed in the analytic system 4018, and feedback maybe provided through the learning feedback system 4012, so that signalsmay be provided based on the right collection or package of sensors andinputs, at the right time and in the right manner, to optimize theeffectiveness of the haptic system 4202. This may include rule-based ormodel-based feedback (such as providing outputs that correspond in somelogical fashion to the source data that is being conveyed). Inembodiments, a cognitive haptic system may be provided, where selectionof inputs or triggers for haptic feedback, selection of outputs, timing,intensity levels, durations, and other parameters (or weights applied tothem) may be varied in a process of variation, promotion, and selection(such as using genetic programming) with feedback based on real worldresponses to feedback in actual situations or based on results ofsimulation and testing of user behavior. Thus, an adaptive hapticinterface for the data collection system 102 is provided, which maylearn and adapt feedback to satisfy requirements and to optimize theimpact on user behavior, such as for overall system outcomes, datacollection outcomes, analytic outcomes, and the like.

Methods and systems are disclosed herein for a presentation layer forAR/VR industrial glasses, where heat map elements are presented based onpatterns and/or parameters in collected data. Methods and systems aredisclosed herein for condition-sensitive, self-organized tuning of AR/VRinterfaces based on feedback metrics and/or training in industrialenvironments. In embodiments, any of the data, measures, and the likedescribed throughout this disclosure can be presented by visualelements, overlays, and the like for presentation in the AR/VRinterfaces, such as in industrial glasses, on AR/VR interfaces on smartphones or tablets, on AR/VR interfaces on data collectors (which may beembodied in smart phones or tablets), on displays located on machines orcomponents, and/or on displays located in industrial environments.

In embodiments, a platform is provided having heat maps displayingcollected data for AR/VR. In embodiments, a platform is provided havingheat maps 4204 displaying collected data from the data collection system102 for providing input to an AR/VR interface 4208. In embodiments, aheat map interface 4304 is provided as an output for the data collectionsystem 102, such as for handling and providing information forvisualization of various sensor data and other data (such as map data,analog sensor data, and other data), such as to one or more componentsof the data collection system 102 or to another system, such as a mobiledevice, tablet, dashboard, computer, AR/VR device, or the like. The datacollection system 102 may be provided in a form factor suitable fordelivering visual input to a user, such as the presentation of a mapthat includes indicators of levels of analog and digital sensor data(such as data indicating levels of rotation, vibration, heating orcooling, pressure, and many other conditions). In such cases, the datacollection systems 102 may be integrated with equipment, or the likethat are used by individuals responsible for operating or monitoring anindustrial environment. In embodiments, signals from various sensors orinput sources (or selective combinations, permutations, mixes, and thelike, as managed by one or more of the cognitive input selection systems4004, 4014) may provide input data to a heat map. Coordinates mayinclude real world location coordinates (such as geo-location orlocation on a map of an environment), as well as other coordinates, suchas time-based coordinates, frequency-based coordinates, or othercoordinates that allow for representation of analog sensor signals,digital signals, input source information, and various combinations, ina map-based visualization, such that colors may represent varying levelsof input along the relevant dimensions. For example, if a nearbyindustrial machine is overheating, the heat map interface may alert auser by showing a machine in bright red. If a system is experiencingunusual vibrations, the heat map interface may show a different colorfor a visual element for the machine, or it may cause an icon or displayelement representing the machine to vibrate in the interface, callingattention to the element. Clicking, touching, or otherwise interactingwith the map can allow a user to drill down and see underlying sensor orinput data that is used as an input to the heat map display. Thus,through various forms of display, the data collection system 102 mayinform users of the need to attend to one or more devices, machines, orother factors, such as those in an industrial environment, withoutrequiring them to read text-based messages or input. The heat mapinterface, and selection of what outputs should be provided, may beconsidered in the cognitive input selection systems 4004, 4014. Forexample, user behavior (such as responses to inputs or displays) may bemonitored and analyzed in the analytic system 4018, and feedback may beprovided through the learning feedback system 4012, so that signals maybe provided based on the right collection or package of sensors andinputs, at the right time and in the right manner, to optimize theeffectiveness of the heat map UI 4304. This may include rule-based ormodel-based feedback (such as feedback providing outputs that correspondin some logical fashion to the source data that is being conveyed). Inembodiments, a cognitive heat map system may be provided, whereselection of inputs or triggers for heat map displays, selection ofoutputs, colors, visual representation elements, timing, intensitylevels, durations and other parameters (or weights applied to them) maybe varied in a process of variation, promotion and selection (such asselection using genetic programming) with feedback based on real worldresponses to feedback in actual situations or based on results ofsimulation and testing of user behavior. Thus, an adaptive heat mapinterface for the data collection system 102, or data collected thereby,or data handled by the host processing system 112, is provided, whichmay learn and adapt feedback to satisfy requirements and to optimize theimpact on user behavior and reaction, such as for overall systemoutcomes, data collection outcomes, analytic outcomes, and the like.

In embodiments, a platform is provided having automatically tuned AR/VRvisualization of data collected by a data collector. In embodiments, aplatform is provided having an automatically tuned AR/VR visualizationsystem 4308 for visualization of data collected by the data collectionsystem 102, such as the case where the data collection system 102 has anAR/VR interface 4208 or provides input to an AR/VR interface 4308 (suchas a mobile phone positioned in a virtual reality or AR headset, a setof AR glasses, or the like). In embodiments, the AR/VR system 4308 isprovided as an output interface of the data collection system 102, suchas a system for handling and providing information for visualization ofvarious sensor data and other data (such as map data, analog sensordata, and other data), such as to one or more components of the datacollection system 102 or to another system, such as a mobile device,tablet, dashboard, computer, AR/VR device, or the like. The datacollection system 102 may be provided in a form factor suitable fordelivering AR or VR visual, auditory, or other sensory input to a user,such as by presenting one or more displays such as 3D-realisticvisualizations, objects, maps, camera overlays, or other overlayelements, maps and the like that include or correspond to indicators oflevels of analog and digital sensor data (such as data indicating levelsof rotation, vibration, heating or cooling, pressure and many otherconditions, to input sources 116, or the like). In such cases, datacollection systems 102 may be integrated with equipment, or the likethat are used by individuals responsible for operating or monitoring anindustrial environment.

In embodiments, signals from various sensors or input sources (orselective combinations, permutations, mixes, and the like as managed byone or more of the cognitive input selection systems 4004, 4014) mayprovide input data to populate, configure, modify, or otherwisedetermine the AR/VR element. Visual elements may include a wide range oficons, map elements, menu elements, sliders, toggles, colors, shapes,sizes, and the like, for representation of analog sensor signals,digital signals, input source information, and various combinations. Inmany examples, colors, shapes, and sizes of visual overlay elements mayrepresent varying levels of input along the relevant dimensions for asensor or combination of sensors. In further examples, if a nearbyindustrial machine is overheating, an AR element may alert a user byshowing an icon representing that type of machine in flashing red colorin a portion of the display of a pair of AR glasses. If a system isexperiencing unusual vibrations, a virtual reality interface showingvisualization of the components of the machine (such as an overlay of acamera view of the machine with 3D visualization elements) may show avibrating component in a highlighted color, with motion, or the like, toensure the component stands out in a virtual reality environment beingused to help a user monitor or service the machine. Clicking, touching,moving eyes toward, or otherwise interacting with a visual element in anAR/VR interface may allow a user to drilldown and see underlying sensoror input data that is used as an input to the display. Thus, throughvarious forms of display, the data collection system 102 may informusers of the need to attend to one or more devices, machines, or otherfactors (such as in an industrial environment), without requiring themto read text-based messages or input or divert attention from theapplicable environment (whether it is a real environment with ARfeatures or a virtual environment, such as for simulation, training, orthe like).

The AR/VR output interface 4208, and selection and configuration of whatoutputs or displays should be provided, may be handled in the cognitiveinput selection systems 4004, 4014. For example, user behavior (such asresponses to inputs or displays) may be monitored and analyzed in theanalytic system 4018, and feedback may be provided through the learningfeedback system 4012, so that AR/VR display signals may be providedbased on the right collection or package of sensors and inputs, at theright time and in the right manner, to optimize the effectiveness of theAR/VR UI 4308. This may include rule-based or model-based feedback (suchas providing outputs that correspond in some logical fashion to thesource data that is being conveyed). In embodiments, a cognitively tunedAR/VR interface control system 4308 may be provided, where selection ofinputs or triggers for AR/VR display elements, selection of outputs(such as colors, visual representation elements, timing, intensitylevels, durations and other parameters [or weights applied to them]) andother parameters of a VR/AR environment may be varied in a process ofvariation, promotion and selection (such as the use of geneticprogramming) with feedback based on real world responses in actualsituations or based on results of simulation and testing of userbehavior. Thus, an adaptive, tuned AR/VR interface for the datacollection system 102, or data collected thereby 102, or data handled bythe host processing system 112, is provided, which may learn and adaptfeedback to satisfy requirements and to optimize the impact on userbehavior and reaction, such as for overall system outcomes, datacollection outcomes, analytic outcomes, and the like.

As noted above, methods and systems are disclosed herein for continuousultrasonic monitoring, including providing continuous ultrasonicmonitoring of rotating elements and bearings of an energy productionfacility. Embodiments include using continuous ultrasonic monitoring ofan industrial environment as a source for a cloud-deployed patternrecognizer. Embodiments include using continuous ultrasonic monitoringto provide updated state information to a state machine that is used asan input to a cloud-deployed pattern recognizer. Embodiments includemaking available continuous ultrasonic monitoring information to a userbased on a policy declared in a policy engine. Embodiments includestoring continuous ultrasonic monitoring data with other data in a fuseddata structure on an industrial sensor device. Embodiments includemaking a stream of continuous ultrasonic monitoring data from anindustrial environment available as a service from a data marketplace.Embodiments include feeding a stream of continuous ultrasonic monitoringdata into a self-organizing data pool. Embodiments include training amachine learning model to monitor a continuous ultrasonic monitoringdata stream where the model is based on a training set created fromhuman analysis of such a data stream, and is improved based on datacollected on performance in an industrial environment.

Embodiments include a swarm of data collectors that include at least onedata collector for continuous ultrasonic monitoring of an industrialenvironment and at least one other type of data collector. Embodimentsinclude using a distributed ledger to store time-series data fromcontinuous ultrasonic monitoring across multiple devices. Embodimentsinclude collecting a stream of continuous ultrasonic data in aself-organizing data collector, a network-sensitive data collector, aremotely organized data collector, a data collector havingself-organized storage and the like. Embodiments include usingself-organizing network coding to transport a stream of ultrasonic datacollected from an industrial environment. Embodiments include conveyingan indicator of a parameter of a continuously collected ultrasonic datastream via an interface where the interface is one of a sensoryinterface of a wearable device, a heat map visual interface of awearable device, an interface that operates with self-organized tuningof the interface layer, and the like.

As noted above, methods and systems are disclosed herein forcloud-based, machine pattern recognition based on fusion of remoteanalog industrial sensors. Embodiments include taking input from aplurality of analog sensors disposed in an industrial environment,multiplexing the sensors into a multiplexed data stream, feeding thedata stream into a cloud-deployed machine learning facility, andtraining a model of the machine learning facility to recognize a definedpattern associated with the industrial environment. Embodiments includeusing a cloud-based pattern recognizer on input states from a statemachine that characterizes states of an industrial environment.Embodiments include deploying policies by a policy engine that governwhat data can be used by what users and for what purpose in cloud-based,machine learning. Embodiments include using a cloud-based platform toidentify patterns in data across a plurality of data pools that containdata published from industrial sensors. Embodiments include training amodel to identify preferred sensor sets to diagnose a condition of anindustrial environment, where a training set is created by a human userand the model is improved based on feedback from data collected aboutconditions in an industrial environment.

Embodiments include a swarm of data collectors that is governed by apolicy that is automatically propagated through the swarm. Embodimentsinclude using a distributed ledger to store sensor fusion informationacross multiple devices. Embodiments include feeding input from a set ofdata collectors into a cloud-based pattern recognizer that uses datafrom multiple sensors for an industrial environment. The data collectorsmay be self-organizing data collectors, network-sensitive datacollectors, remotely organized data collectors, a set of data collectorshaving self-organized storage, and the like. Embodiments include asystem for data collection in an industrial environment withself-organizing network coding for data transport of data fused frommultiple sensors in the environment. Embodiments include conveyinginformation formed by fusing inputs from multiple sensors in anindustrial data collection system in an interface such as amulti-sensory interface, a heat map interface, an interface thatoperates with self-organized tuning of the interface layer, and thelike.

As noted above, methods and systems are disclosed herein forcloud-based, machine pattern analysis of state information from multipleanalog industrial sensors to provide anticipated state information foran industrial system. Embodiments include using a policy engine todetermine what state information can be used for cloud-based machineanalysis. Embodiments include feeding inputs from multiple devices thathave fused and on-device storage of multiple sensor streams into acloud-based pattern recognizer to determine an anticipated state of anindustrial environment. Embodiments include making an output, such asanticipated state information, from a cloud-based machine patternrecognizer that analyzes fused data from remote, analog industrialsensors available as a data service in a data marketplace. Embodimentsinclude using a cloud-based pattern recognizer to determine ananticipated state of an industrial environment based on data collectedfrom data pools that contain streams of information from machines in theenvironment. Embodiments include training a model to identify preferredstate information to diagnose a condition of an industrial environment,where a training set is created by a human user and the model isimproved based on feedback from data collected about conditions in anindustrial environment. Embodiments include a swarm of data collectorsthat feeds a state machine that maintains current state information foran industrial environment. Embodiments include using a distributedledger to store historical state information for fused sensor states aself-organizing data collector that feeds a state machine that maintainscurrent state information for an industrial environment. Embodimentsinclude a data collector that feeds a state machine that maintainscurrent state information for an industrial environment where the datacollector may be a network sensitive data collector, a remotelyorganized data collector, a data collector with self-organized storage,and the like. Embodiments include a system for data collection in anindustrial environment with self-organizing network coding for datatransport and maintains anticipated state information for theenvironment. Embodiments include conveying anticipated state informationdetermined by machine learning in an industrial data collection systemin an interface where the interface may be one or more of a multisensoryinterface, a heat map interface an interface that operates withself-organized tuning of the interface layer, and the like.

As noted above, methods and systems are disclosed herein for acloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices, including a cloud-based policy automationengine for IoT, enabling creation, deployment and management of policiesthat apply to IoT devices. Policies can relate to data usage to anon-device storage system that stores fused data from multiple industrialsensors, or what data can be provided to whom in a self-organizingmarketplace for IoT sensor data. Policies can govern how aself-organizing swarm or data collector should be organized for aparticular industrial environment, how a network-sensitive datacollector should use network bandwidth for a particular industrialenvironment, how a remotely organized data collector should collect, andmake available, data relating to a specified industrial environment, orhow a data collector should self-organize storage for a particularindustrial environment. Policies can be deployed across a set ofself-organizing pools of data that contain data streamed from industrialsensing devices to govern use of data from the pools or stored on adevice that governs use of storage capabilities of the device for adistributed ledger. Embodiments include training a model to determinewhat policies should be deployed in an industrial data collectionsystem. Embodiments include a system for data collection in anindustrial environment with a policy engine for deploying policy withinthe system and, optionally, self-organizing network coding for datatransport. In certain embodiments, a policy applies to how data will bepresented in a multi-sensory interface, a heat map visual interface, orin an interface that operates with self-organized tuning of theinterface layer.

As noted above, methods and systems are disclosed herein for on-devicesensor fusion and data storage for industrial IoT devices, such as anindustrial data collector, including self-organizing, remotelyorganized, or network-sensitive industrial data collectors, where datafrom multiple sensors is multiplexed at the device for storage of afused data stream. Embodiments include a self-organizing marketplacethat presents fused sensor data that is extracted from on-device storageof IoT devices. Embodiments include streaming fused sensor informationfrom multiple industrial sensors and from an on-device data storagefacility to a data pool. Embodiments include training a model todetermine what data should be stored on a device in a data collectionenvironment. Embodiments include a self-organizing swarm of industrialdata collectors that organize among themselves to optimize datacollection, where at least some of the data collectors have on-devicestorage of fused data from multiple sensors. Embodiments include storingdistributed ledger information with fused sensor information on anindustrial IoT device. Embodiments include a system for data collectionwith on-device sensor fusion, such as of industrial sensor data and,optionally, self-organizing network coding for data transport, wheredata structures are stored to support alternative, multi-sensory modesof presentation, visual heat map modes of presentation, and/or aninterface that operates with self-organized tuning of the interfacelayer.

As noted above, methods and systems are disclosed herein for aself-organizing data marketplace for industrial IoT data, whereavailable data elements are organized in the marketplace for consumptionby consumers based on training a self-organizing facility with atraining set and feedback from measures of marketplace success.Embodiments include organizing a set of data pools in a self-organizingdata marketplace based on utilization metrics for the data pools.Embodiments include training a model to determine pricing for data in adata marketplace. The data marketplace is fed with data streams from aself-organizing swarm of industrial data collectors, a set of industrialdata collectors that have self-organizing storage, or self-organizing,network-sensitive, or remotely organized industrial data collectors.Embodiments include using a distributed ledger to store transactionaldata for a self-organizing marketplace for industrial IoT data.Embodiments include using self-organizing network coding for datatransport to a marketplace for sensor data collected in industrialenvironments. Embodiments include providing a library of data structuressuitable for presenting data in alternative, multi-sensory interfacemodes in a data marketplace, in heat map visualization, and/or ininterfaces that operate with self-organized tuning of the interfacelayer.

As noted above, methods and systems are disclosed herein forself-organizing data pools such as those that self-organize based onutilization and/or yield metrics that may be tracked for a plurality ofdata pools. In embodiments, the pools contain data from self-organizingdata collectors. Embodiments include training a model to present themost valuable data in a data marketplace, where training is based onindustry-specific measures of success. Embodiments include populating aset of self-organizing data pools with data from a self-organizing swarmof data collectors. Embodiments include using a distributed ledger tostore transactional information for data that is deployed in data pools,where the distributed ledger is distributed across the data pools.Embodiments include populating a set of self-organizing data pools withdata from a set of network-sensitive or remotely organized datacollectors or a set of data collectors having self-organizing storage.Embodiments include a system for data collection in an industrialenvironment with self-organizing pools for data storage andself-organizing network coding for data transport, such as a system thatincludes a source data structure for supporting data presentation in amulti-sensory interface, in a heat map interface, and/or in an interfacethat operates with self-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for training AImodels based on industry-specific feedback, such as that reflects ameasure of utilization, yield, or impact, where the AI model operates onsensor data from an industrial environment. Embodiments include traininga swarm of data collectors, or data collectors, such as remotelyorganized, self-organizing, or network-sensitive data collectors, basedon industry-specific feedback or network and industrial conditions in anindustrial environment, such as to configure storage. Embodimentsinclude training an AI model to identify and use available storagelocations in an industrial environment for storing distributed ledgerinformation. Embodiments include training a remote organizer for aremotely organized data collector based on industry-specific feedbackmeasures. Embodiments include a system for data collection in anindustrial environment with cloud-based training of a network codingmodel for organizing network coding for data transport or a facilitythat manages presentation of data in a multi-sensory interface, in aheat map interface, and/or in an interface that operates withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for aself-organized swarm of industrial data collectors that organize amongthemselves to optimize data collection based on the capabilities andconditions of the members of the swarm. Embodiments include deployingdistributed ledger data structures across a swarm of data. Datacollectors may be network-sensitive data collectors configured forremote organization or have self-organizing storage. Systems for datacollection in an industrial environment with a swarm can include aself-organizing network coding for data transport. Systems includeswarms that relay information for use in a multi-sensory interface, in aheat map interface, and/or in an interface that operates withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for anindustrial IoT distributed ledger, including a distributed ledgersupporting the tracking of transactions executed in an automated datamarketplace for industrial IoT data. Embodiments include aself-organizing data collector that is configured to distributecollected information to a distributed ledger. Embodiments include anetwork-sensitive data collector that is configured to distributecollected information to a distributed ledger based on networkconditions. Embodiments include a remotely organized data collector thatis configured to distribute collected information to a distributedledger based on intelligent, remote management of the distribution.Embodiments include a data collector with self-organizing local storagethat is configured to distribute collected information to a distributedledger. Embodiments include a system for data collection in anindustrial environment using a distributed ledger for data storage andself-organizing network coding for data transport. In embodiments, datastorage is of a data structure supporting a haptic interface for datapresentation, a heat map interface for data presentation, and/or aninterface that operates with self-organized tuning of the interfacelayer.

As noted above, methods and systems are disclosed herein for aself-organizing collector, including a self-organizing, multi-sensordata collector that can optimize data collection, power and/or yieldbased on conditions in its environment, and is optionally responsive toremote organization. Embodiments include a self-organizing datacollector that organizes at least in part based on network conditions.Embodiments include a self-organizing data collector withself-organizing storage for data collected in an industrial datacollection environment. Embodiments include a system for data collectionin an industrial environment with self-organizing data collection andself-organizing network coding for data transport. Embodiments include asystem for data collection in an industrial environment with aself-organizing data collector that feeds a data structure supporting ahaptic or multi-sensory wearable interface for data presentation, a heatmap interface for data presentation, and/or an interface that operateswith self-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for anetwork-sensitive collector, including a network condition-sensitive,self-organizing, multi-sensor data collector that can optimize based onbandwidth, quality of service, pricing, and/or other network conditions.Embodiments include a remotely organized, network condition-sensitiveuniversal data collector that can power up and down sensor interfacesbased on need and/or conditions identified in an industrial datacollection environment, including network conditions. Embodimentsinclude a network-condition sensitive data collector withself-organizing storage for data collected in an industrial datacollection environment. Embodiments include a network-conditionsensitive data collector with self-organizing network coding for datatransport in an industrial data collection environment. Embodimentsinclude a system for data collection in an industrial environment with anetwork-sensitive data collector that relays a data structure supportinga haptic wearable interface for data presentation, a heat map interfacefor data presentation, and/or an interface that operates withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for a remotelyorganized universal data collector that can power up and down sensorinterfaces based on need and/or conditions identified in an industrialdata collection environment. Embodiments include a remotely organizeduniversal data collector with self-organizing storage for data collectedin an industrial data collection environment. Embodiments include asystem for data collection in an industrial environment with remotecontrol of data collection and self-organizing network coding for datatransport. Embodiments include a remotely organized data collector forstoring sensor data and delivering instructions for use of the data in ahaptic or multi-sensory wearable interface, in a heat map visualinterface, and/or in an interface that operates with self-organizedtuning of the interface layer.

As noted above, methods and systems are disclosed herein forself-organizing storage for a multi-sensor data collector, includingself-organizing storage for a multi-sensor data collector for industrialsensor data. Embodiments include a system for data collection in anindustrial environment with self-organizing data storage andself-organizing network coding for data transport. Embodiments include adata collector with self-organizing storage for storing sensor data andinstructions for translating the data for use in a haptic wearableinterface, in a heat map presentation interface, and/or in an interfacethat operates with self-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein forself-organizing network coding for a multi-sensor data network,including self-organizing network coding for a data network thattransports data from multiple sensors in an industrial data collectionenvironment. The system includes a data structure supporting a hapticwearable interface for data presentation, a heat map interface for datapresentation, and/or self-organized tuning of an interface layer fordata presentation.

As noted above, methods and systems are disclosed herein for a haptic ormulti-sensory user interface, including a wearable haptic ormulti-sensory user interface for an industrial sensor data collector,with vibration, heat, electrical, and/or sound outputs. Embodimentsinclude a wearable haptic user interface for conveying industrial stateinformation from a data collector, with vibration, heat, electrical,and/or sound outputs. The wearable also has a visual presentation layerfor presenting a heat map that indicates a parameter of the data.Embodiments include condition-sensitive, self-organized tuning of AR/VRinterfaces and multi-sensory interfaces based on feedback metrics and/ortraining in industrial environments.

As noted above, methods and systems are disclosed herein for apresentation layer for AR/VR industrial glasses, where heat map elementsare presented based on patterns and/or parameters in collected data.Embodiments include condition-sensitive, self-organized tuning of a heatmap AR/VR interface based on feedback metrics and/or training inindustrial environments. As noted above, methods and systems aredisclosed herein for condition-sensitive, self-organized tuning of AR/VRinterfaces based on feedback metrics and/or training in industrialenvironments.

The following illustrative claims describe certain embodiments of thepresent disclosure. The data collection system mentioned in thefollowing disclosure may be a local data collection system 102, the hostprocessing system 112 (e.g., using a cloud platform), or a combinationof a local system and a host system. In embodiments, a data collectionsystem or data collection and processing system is provided having theuse of an analog crosspoint switch for collecting data having variablegroups of analog sensor inputs and, in some embodiments, having IPfront-end-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio, multiplexer continuous monitoring alarmingfeatures, the use of distributed CPLD chips with a dedicated bus forlogic control of multiple MUX and data acquisition sections,high-amperage input capability using solid state relays and designtopology, power-down capability of at least one of an analog sensorchannel and of a component board, unique electrostatic protection fortrigger and vibration inputs, and/or precise voltage reference for A/Dzero reference.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation, digital derivation of phase relative to input and triggerchannels using on-board timers, a peak-detector for auto-scaling that isrouted into a separate analog-to-digital converter for peak detection,the routing of a trigger channel that is either raw or buffered intoother analog channels, the use of higher input oversampling fordelta-sigma A/D for lower sampling rate outputs to minimize AA filterrequirements, and/or the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having long blocks of dataat a high-sampling rate, as opposed to multiple sets of data taken atdifferent sampling rates, storage of calibration data with a maintenancehistory on-board card set, a rapid route creation capability usinghierarchical templates, intelligent management of data collection bands,and/or a neural net expert system using intelligent management of datacollection bands.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having use of a databasehierarchy in sensor data analysis, an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system, a graphical approach for back-calculation definition,proposed bearing analysis methods, torsional vibrationdetection/analysis utilizing transitory signal analysis, and/or improvedintegration using both analog and digital methods.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment, data acquisition parking features, a self-sufficient dataacquisition box, SD card storage, extended onboard statisticalcapabilities for continuous monitoring, the use of ambient, local andvibration noise for prediction, smart route changes based on incomingdata or alarms to enable simultaneous dynamic data for analysis orcorrelation, smart ODS and transfer functions, a hierarchicalmultiplexer, identification of sensor overload, and/or RF identificationand an inclinometer.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having continuous ultrasonicmonitoring, cloud-based, machine pattern recognition based on the fusionof remote, analog industrial sensors, cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system,cloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices, on-device sensor fusion and data storagefor industrial IoT devices, a self-organizing data marketplace forindustrial IoT data, self-organization of data pools based onutilization and/or yield metrics, training AI models based onindustry-specific feedback, a self-organized swarm of industrial datacollectors, an IoT distributed ledger, a self-organizing collector, anetwork-sensitive collector, a remotely organized collector, aself-organizing storage for a multi-sensor data collector, aself-organizing network coding for multi-sensor data network, a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical, and/or sound outputs, heat maps displayingcollected data for AR/VR, and/or automatically tuned AR/VR visualizationof data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving IP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having at least oneof: multiplexer continuous monitoring alarming features; IP front-endsignal conditioning on a multiplexer for improved signal-to-noise ratio;the use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections. In embodiments, a datacollection and processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving at least one of: high-amperage input capability using solid staterelays and design topology; power-down capability of at least one analogsensor channel and of a component board; unique electrostatic protectionfor trigger and vibration inputs; precise voltage reference for A/D zeroreference; and a phase-lock loop band-pass tracking filter for obtainingslow-speed RPMs and phase information. In embodiments, a data collectionand processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving at least one of: digital derivation of phase relative to inputand trigger channels using on-board timers; a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection; routing of a trigger channel that is either raw orbuffered into other analog channels; the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements; and the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling. Inembodiments, a data collection and processing system is provided havingIP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio and having at least one of: long blocks of data ata high-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates; storage of calibration data with a maintenancehistory on-board card set; a rapid route creation capability usinghierarchical templates; intelligent management of data collection bands;and a neural net expert system using intelligent management of datacollection bands. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having at least oneof: use of a database hierarchy in sensor data analysis; an expertsystem GUI graphical approach to defining intelligent data collectionbands and diagnoses for the expert system; and a graphical approach forback-calculation definition.

In embodiments, a data collection and processing system is providedhaving IP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio and having at least one of: proposed bearinganalysis methods; torsional vibration detection/analysis utilizingtransitory signal; improved integration using both analog and digitalmethods; adaptive scheduling techniques for continuous monitoring ofanalog data in a local environment; data acquisition parking features; aself-sufficient data acquisition box; and SD card storage. Inembodiments, a data collection and processing system is provided havingIP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio and having at least one of: extended onboardstatistical capabilities for continuous monitoring; the use of ambient,local, and vibration noise for prediction; smart route changes based onincoming data or alarms to enable simultaneous dynamic data for analysisor correlation; smart ODS and transfer functions; and a hierarchicalmultiplexer. In embodiments, a data collection and processing system isprovided having IP front-end signal conditioning on a multiplexer forimproved signal-to-noise ratio and having at least one of:identification of sensor overload; RF identification and aninclinometer; continuous ultrasonic monitoring; machine patternrecognition based on the fusion of remote, analog industrial sensors;and cloud-based, machine pattern analysis of state information frommultiple analog industrial sensors to provide anticipated stateinformation for an industrial system. In embodiments, a data collectionand processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving at least one of: cloud-based policy automation engine for IoT,with creation, deployment, and management of IoT devices; on-devicesensor fusion and data storage for industrial IoT devices; aself-organizing data marketplace for industrial IoT data; andself-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having IP front-end signal conditioning on a multiplexer forimproved signal-to-noise ratio and having at least one of: training AImodels based on industry-specific feedback; a self-organized swarm ofindustrial data collectors; an IoT distributed ledger; a self-organizingcollector; and a network-sensitive collector. In embodiments, a datacollection and processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving at least one of: a remotely organized collector; aself-organizing storage for a multi-sensor data collector; aself-organizing network coding for multi-sensor data network; a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical, and/or sound outputs; heat maps displayingcollected data for AR/VR; and automatically tuned AR/VR visualization ofdata collected by a data collector.

In embodiments, a data collection and processing system is providedhaving multiplexer continuous monitoring alarming features. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having at leastone of: the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections; high-amperageinput capability using solid state relays and design topology;power-down capability of at least one of an analog sensor channel and/orof a component board; unique electrostatic protection for trigger andvibration inputs; and precise voltage reference for A/D zero reference.In embodiments, a data collection and processing system is providedhaving multiplexer continuous monitoring alarming features and having atleast one of: a phase-lock loop band-pass tracking filter for obtainingslow-speed RPMs and phase information; digital derivation of phaserelative to input and trigger channels using on-board timers; apeak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection; and routing of a triggerchannel that is either raw or buffered into other analog channels. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having at leastone of: the use of higher input oversampling for delta-sigma A/D forlower sampling rate outputs to minimize AA filter requirements; the useof a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling; long blocks of data at a high-sampling rate as opposed tomultiple sets of data taken at different sampling rates; storage ofcalibration data with a maintenance history on-board card set; and arapid route creation capability using hierarchical templates. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having at leastone of: intelligent management of data collection bands; a neural netexpert system using intelligent management of data collection bands; useof a database hierarchy in sensor data analysis; and an expert systemGUI graphical approach to defining intelligent data collection bands anddiagnoses for the expert system. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having at least one of: a graphical approach forback-calculation definition; proposed bearing analysis methods;torsional vibration detection/analysis utilizing transitory signalanalysis; and improved integration using both analog and digitalmethods. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving at least one of adaptive scheduling techniques for continuousmonitoring of analog data in a local environment; data acquisitionparking features; a self-sufficient data acquisition box; and SD cardstorage. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving at least one of: extended onboard statistical capabilities forcontinuous monitoring; the use of ambient, local and vibration noise forprediction; smart route changes based on incoming data or alarms toenable simultaneous dynamic data for analysis or correlation; and smartODS and transfer functions. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having at least one of: a hierarchicalmultiplexer; identification of sensor overload; RF identification, andan inclinometer; cloud-based, machine pattern recognition based on thefusion of remote, analog industrial sensors; and machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having at leastone of: cloud-based policy automation engine for IoT, with creation,deployment, and management of IoT devices; on-device sensor fusion anddata storage for industrial IoT devices; a self-organizing datamarketplace for industrial IoT data; self-organization of data poolsbased on utilization and/or yield metrics; and training AI models basedon industry-specific feedback. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having at least one of: a self-organized swarm ofindustrial data collectors; an IoT distributed ledger; a self-organizingcollector; a network-sensitive collector; and a remotely organizedcollector. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving at least one of: a self-organizing storage for a multi-sensordata collector; and a self-organizing network coding for multi-sensordata network. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving at least one of: a wearable haptic user interface for anindustrial sensor data collector, with vibration, heat, electrical,and/or sound outputs; heat maps displaying collected data for AR/VR; andautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a data collection and processing system is providedhaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having high-amperage inputcapability using solid state relays and design topology. In embodiments,a data collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having power-down capability of atleast one of an analog sensor channel and of a component board. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having uniqueelectrostatic protection for trigger and vibration inputs. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having precise voltagereference for A/D zero reference. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a phase-lock loop band-pass trackingfilter for obtaining slow-speed RPMs and phase information. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having digitalderivation of phase relative to input and trigger channels usingon-board timers. In embodiments, a data collection and processing systemis provided having the use of distributed CPLD chips with dedicated busfor logic control of multiple MUX and data acquisition sections andhaving a peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection. In embodiments, a datacollection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having routing of a triggerchannel that is either raw or buffered into other analog channels. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having the use ofhigher input oversampling for delta-sigma A/D for lower sampling rateoutputs to minimize AA filter requirements. In embodiments, a datacollection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having long blocks ofdata at a high-sampling rate as opposed to multiple sets of data takenat different sampling rates. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having storage of calibration data with amaintenance history on-board card set. In embodiments, a data collectionand processing system is provided having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections and having a rapid route creation capability usinghierarchical templates. In embodiments, a data collection and processingsystem is provided having the use of distributed CPLD chips withdedicated bus for logic control of multiple MUX and data acquisitionsections and having intelligent management of data collection bands. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having a neural netexpert system using intelligent management of data collection bands. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having use of adatabase hierarchy in sensor data analysis. In embodiments, a datacollection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a graphical approach forback-calculation definition. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having proposed bearing analysis methods. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having torsionalvibration detection/analysis utilizing transitory signal analysis. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having improvedintegration using both analog and digital methods. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and havingdata acquisition parking features. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a self-sufficient data acquisition box.In embodiments, a data collection and processing system is providedhaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections and having SD cardstorage. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and havingextended onboard statistical capabilities for continuous monitoring. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having the use ofambient, local and vibration noise for prediction. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having smart route changes basedon incoming data or alarms to enable simultaneous dynamic data foranalysis or correlation. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having smart ODS and transfer functions. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having a hierarchicalmultiplexer. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and havingidentification of sensor overload. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having RF identification and an inclinometer.In embodiments, a data collection and processing system is providedhaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections and havingcontinuous ultrasonic monitoring. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having cloud-based, machinepattern analysis of state information from multiple analog industrialsensors to provide anticipated state information for an industrialsystem. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and havingcloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having on-device sensor fusion and data storagefor industrial IoT devices. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a self-organizing data marketplace forindustrial IoT data. In embodiments, a data collection and processingsystem is provided having the use of distributed CPLD chips withdedicated bus for logic control of multiple MUX and data acquisitionsections and having self-organization of data pools based on utilizationand/or yield metrics. In embodiments, a data collection and processingsystem is provided having the use of distributed CPLD chips withdedicated bus for logic control of multiple MUX and data acquisitionsections and having training AI models based on industry-specificfeedback. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having an IoT distributed ledger.In embodiments, a data collection and processing system is providedhaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a network-sensitive collector. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having a remotelyorganized collector. In embodiments, a data collection and processingsystem is provided having the use of distributed CPLD chips withdedicated bus for logic control of multiple MUX and data acquisitionsections and having a self-organizing storage for a multi-sensor datacollector. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and having aself-organizing network coding for multi-sensor data network. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical, and/or sound outputs. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having heat maps displayingcollected data for AR/VR. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having automatically tuned AR/VR visualizationof data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving one or more of high-amperage input capability using solid staterelays and design topology, power-down capability of at least one of ananalog sensor channel and of a component board, unique electrostaticprotection for trigger and vibration inputs, precise voltage referencefor A/D zero reference, a phase-lock loop band-pass tracking filter forobtaining slow-speed RPMs and phase information, digital derivation ofphase relative to input and trigger channels using on-board timers, apeak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection, routing of a triggerchannel that is either raw or buffered into other analog channels, theuse of higher input oversampling for delta-sigma A/D for lower samplingrate outputs to minimize anti-aliasing (AA) filter requirements, the useof a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling, long blocks of data at a high-sampling rate as opposed tomultiple sets of data taken at different sampling rates, storage ofcalibration data with a maintenance history on-board card set, a rapidroute creation capability using hierarchical templates, intelligentmanagement of data collection bands, a neural net expert system usingintelligent management of data collection bands, use of a databasehierarchy in sensor data analysis, an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system, a graphical approach for back-calculation definition,proposed bearing analysis methods, torsional vibrationdetection/analysis utilizing transitory signal analysis, improvedintegration using both analog and digital methods, adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment, data acquisition parking features, a self-sufficient dataacquisition box, SD card storage, extended onboard statisticalcapabilities for continuous monitoring, the use of ambient, local, andvibration noise for prediction, smart route changes based on incomingdata or alarms to enable simultaneous dynamic data for analysis orcorrelation, smart ODS and transfer functions, a hierarchicalmultiplexer, identification of sensor overload, RF identification and aninclinometer, continuous ultrasonic monitoring, cloud-based, machinepattern recognition based on fusion of remote, analog industrialsensors, cloud-based, machine pattern analysis of state information frommultiple analog industrial sensors to provide anticipated stateinformation for an industrial system, cloud-based policy automationengine for IoT, with creation, deployment, and management of IoTdevices, on-device sensor fusion and data storage for industrial IoTdevices, a self-organizing data marketplace for industrial IoT data,self-organization of data pools based on utilization and/or yieldmetrics, training AI models based on industry-specific feedback, aself-organized swarm of industrial data collectors, an IoT distributedledger, a self-organizing collector, a network-sensitive collector, aremotely organized collector, a self-organizing storage for amulti-sensor data collector, a self-organizing network coding formulti-sensor data network, a wearable haptic user interface for anindustrial sensor data collector, with vibration, heat, electrical,and/or sound outputs, heat maps displaying collected data for AR/VR, orautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a platform is provided having one or more ofcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors, cloud-based, machine pattern analysis ofstate information from multiple analog industrial sensors to provideanticipated state information for an industrial system, a cloud-basedpolicy automation engine for IoT, with creation, deployment, andmanagement of IoT devices, on-device sensor fusion and data storage forindustrial IoT devices, a self-organizing data marketplace forindustrial IoT data, self-organization of data pools based onutilization and/or yield metrics, training AI models based onindustry-specific feedback, a self-organized swarm of industrial datacollectors, an IoT distributed ledger, a self-organizing collector, anetwork-sensitive collector, a remotely organized collector, aself-organizing storage for a multi-sensor data collector, aself-organizing network coding for multi-sensor data network, a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical, and/or sound outputs, heat maps displayingcollected data for AR/VR, or automatically tuned AR/VR visualization ofdata collected by a data collector.

With regard to FIG. 18, a range of existing data sensing and processingsystems with industrial sensing, processing, and storage systems 4500include a streaming data collector 4510 that may be configured to acceptdata in a range of formats as described herein. In embodiments, therange of formats can include a data format A 4520, a data format B 4522,a data format C 4524, and a data format D 4528 that may be sourced froma range of sensors. Moreover, the range of sensors can include aninstrument A 4540, an instrument B 4542, an instrument C 4544, and aninstrument D 4548. The streaming data collector 4510 may be configuredwith processing capabilities that enable access to the individualformats while leveraging the streaming, routing, self-organizingstorage, and other capabilities described herein.

FIG. 19 depicts methods and systems 4600 for industrial machine sensordata streaming collection, processing, and storage that facilitate useof a streaming data collector 4610 to collect and obtain data from thelegacy instruments 4620 and streaming instruments 4622. The legacyinstruments 4620 and their data methodologies may capture and providedata that is limited in scope, due to the legacy systems and acquisitionprocedures, such as existing data methodologies described above herein,to a particular range of frequencies and the like. The streaming datacollector 4610 may be configured to capture streaming instrument data4632 as well as legacy instrument data 4630. The streaming datacollector 4610 may also be configured to capture current streaming ofthe legacy instruments 4620 and the streaming instruments 4622 andsensors using current and legacy data methodologies. These embodimentsmay be useful in transition applications from the legacy instruments andprocessing to the streaming instruments and processing that may becurrent or desired instruments or methodologies. In embodiments, thestreaming data collector 4610 may be configured to process the legacyinstrument data 4630 so that it can be stored compatibly with thestreamed instrument data 4632. The streaming data collector 4610 mayprocess or parse the streamed instrument data 4632 based on the legacyinstrument data 4630 to produce at least one extraction of the streameddata 4642 that is compatible with the legacy instrument data 4630 thatcan be processed into translated legacy data 4640. In embodiments,extracted data 4650 that can include extracted portions of translatedlegacy data 4652 and streamed data 4654 may be stored in a format thatfacilitates access and processing by legacy instrument data processingand further processing that can emulate legacy instrument dataprocessing methods, and the like. In embodiments, the portions of thetranslated legacy data 4652 may also be stored in a format thatfacilitates processing with different methods that can take advantage ofthe greater frequencies, resolution, and volume of data possible with astreaming instrument.

FIG. 20 depicts alternate embodiments descriptive of methods and systems4700 for industrial machine sensor data streaming, collection,processing, and storage that facilitate integration of legacyinstruments and processing. In embodiments, a streaming data collector4710 may be connected with an industrial machine 4712 and may include aplurality of sensors, such as streaming sensors 4720 and 4722 that maybe configured to sense aspects of the industrial machine 4712 associatedwith at least one moving part of the machine 4712. The sensors 4720 and4722 (or more) may communicate with one or more streaming devices 4740that may facilitate streaming data from one or more of the sensors tothe streaming data collector 4710. In embodiments, the industrialmachine 4712 may also interface with or include one or more legacyinstruments 4730 that may capture data associated with one or moremoving parts of the industrial machine 4712 and store that data into alegacy data storage facility 4732.

In embodiments, a frequency and/or resolution detection facility 4742may be configured to facilitate detecting information about legacyinstrument sourced data, such as a frequency range of the data or aresolution of the data, and the like. The detection facility 4742 mayoperate on data directly from the legacy instruments 4730 or from datastored in the legacy storage facility 4732. The detection facility 4742may communicate information detected about the legacy instruments 4730,its sourced data, and its stored data 4732, or the like to the streamingdata collector 4710. Alternatively, the detection facility 4742 mayaccess information, such as information about frequency ranges,resolution, and the like that characterizes the sourced data from thelegacy instrument 4730 and/or may be accessed from a portion of thelegacy storage facility 4732.

In embodiments, the streaming data collector 4710 may be configured withone or more automatic processors, algorithms, and/or other datamethodologies to match up information captured by the one or more legacyinstruments 4730 with a portion of data being provided by the one ormore streaming devices 4740 from the one or more industrial machines4712. Data from streaming devices 4740 may include a wider range offrequencies and resolutions than the sourced data of legacy instruments4730 and, therefore, filtering and other such functions can beimplemented to extract data from the streaming devices 4740 thatcorresponds to the sourced data of the legacy instruments 4730 inaspects such as frequency range, resolution, and the like. Inembodiments, the configured streaming data collector 4710 may produce aplurality of streams of data, including a stream of data that maycorrespond to the stream of data from the streaming device 4740 and aseparate stream of data that is compatible, in some aspects, with thelegacy instrument sourced data and the infrastructure to ingest andautomatically process it. Alternatively, the streaming data collector4710 may output data in modes other than as a stream, such as batches,aggregations, summaries, and the like.

The configured streaming data collector 4710 may communicate with astream storage facility 4764 for storing at least one of the dataoutputs from the streaming device 4710 and data extracted therefrom thatmay be compatible, in some aspects, with the sourced data of the legacyinstruments 4730. A legacy compatible output of the configured streamingdata collector 4710 may also be provided to a format adaptor facility4748, 4760 that may configure, adapt, reformat, and make otheradjustments to the legacy compatible data so that it can be stored in alegacy compatible storage facility 4762 so that legacy processingfacilities 4744 may execute data processing methods on data in thelegacy compatible storage facility 4762 and the like that are configuredto process the sourced data of the legacy instruments 4730. Inembodiments in which legacy compatible data is stored in the streamstorage facility 4764, legacy processing facility 4744 may alsoautomatically process this data after optionally being processed byformat adaptor 4760. By arranging the data collection, streaming,processing, formatting, and storage elements to provide data in a formatthat is fully compatible with legacy instrument sourced data, transitionfrom a legacy system can be simplified, and the sourced data from legacyinstruments can be easily compared to newly acquired data (with morecontent) without losing the legacy value of the sourced data from thelegacy instruments 4730.

FIG. 21 depicts alternate embodiments of a methods and systems 4800described herein for industrial machine sensor data streaming,collection, processing, and storage that may be compatible with legacyinstrument data collection and processing. In embodiments, processingindustrial machine sensed data may be accomplished in a variety of waysincluding aligning legacy and streaming sources of data, such as byaligning stored legacy and streaming data; aligning stored legacy datawith a stream of sensed data; and aligning legacy and streamed data asit is being collected. In embodiments, an industrial machine 4810 mayinclude, communicate with, or be integrated with one or more stream datasensors 4820 that may sense aspects of the industrial machine 4810 suchas aspects of one or more moving parts of the machine. The industrialmachine 4810 may also communicate with, include, or be integrated withone or more legacy data sensors 4830 that may sense similar aspects ofthe industrial machine 4810. In embodiments, the one or more legacy datasensors 4830 may provide sensed data to one or more legacy datacollectors 4840. The stream data sensors 4820 may produce an output thatencompasses all aspects of (i.e., a richer signal) and is compatiblewith sensed data from the legacy data sensors 4830. The stream datasensors 4820 may provide compatible data to the legacy data collector4840. By mimicking the legacy data sensors 4830 or their data streams,the stream data sensors 4820 may replace (or serve as suitable duplicatefor) one or more legacy data sensors, such as during an upgrade of thesensing and processing system of an industrial machine. Frequency range,resolution, and the like may be mimicked by the stream data so as toensure that all forms of legacy data are captured or can be derived fromthe stream data. In embodiments, format conversion, if needed, can alsobe performed by the stream data sensors 4820. The stream data sensors4820 may also produce an alternate data stream that is suitable forcollection by the stream data collector 4850. In embodiments, such analternate data stream may be a superset of the legacy data sensor datain at least one or more of: frequency range, resolution, duration ofsensing the data, and the like.

In embodiments, an industrial machine sensed data processing facility4860 may execute a wide range of sensed data processing methods, some ofwhich may be compatible with the data from legacy data sensors 4830 andmay produce outputs that may meet legacy sensed data processingrequirements. To facilitate use of a wide range of data processingcapabilities of the processing facility 4860, legacy and stream data mayneed to be aligned so that a compatible portion of stream data may beextracted for processing with legacy compatible methods and the like. Inembodiments, FIG. 21 depicts three different techniques for aligningstream data to legacy data. A first alignment methodology 4862 includesaligning legacy data output by the legacy data collector 4840 withstream data output by the stream data collector 4850. As data isprovided by the legacy data collector 4840, aspects of the data may bedetected, such as resolution, frequency, duration, and the like, and maybe used as control for a processing method that identifies portions of astream of data from the stream data collector 4850 that are purposelycompatible with the legacy data. The processing facility 4860 may applyone or more legacy compatible methods on the identified portions of thestream data to extract data that can be easily compared to or referencedagainst the legacy data.

In embodiments, a second alignment methodology 4864 may involve aligningstreaming data with data from a legacy storage facility 4882. Inembodiments, a third alignment methodology 4868 may involve aligningstored stream data from a stream storage facility 4884 with legacy datafrom the legacy data storage facility 4882. In each of the methodologies4862, 4864, 4868, alignment data may be determined by processing thelegacy data to detect aspects such as resolution, duration, frequencyrange, and the like. Alternatively, alignment may be performed by analignment facility, such as facilities using the methodologies 4862,4864, 4868 that may receive or may be configured with legacy datadescriptive information such as legacy frequency range, duration,resolution, and the like.

In embodiments, an industrial machine sensing data processing facility4860 may have access to legacy compatible methods and algorithms thatmay be stored in a legacy data methodology storage facility 4880. Thesemethodologies, algorithms, or other data in the legacy algorithm storagefacility 4880 may also be a source of alignment information that couldbe communicated by the industrial machine sensed data processingfacility 4860 to the various alignment facilities having themethodologies 4862, 4864, 4868. By having access to legacy compatiblealgorithms and methodologies, the data processing facility 4860 mayfacilitate processing legacy data, streamed data that is compatible withlegacy data, or portions of streamed data that represent the legacy datato produce legacy compatible analytics.

In embodiments, the data processing facility 4860 may execute a widerange of other sensed data processing methods, such as waveletderivations and the like, to produce streamed data analytics 4892. Inembodiments, the streaming data collectors 102, 4510, 4610, 4710 (FIGS.3, 6, 18, 19, 20) or data processing facility 4860 may include portablealgorithms, methodologies, and inputs that may be defined and extractedfrom data streams. In many examples, a user or enterprise may alreadyhave existing and effective methods related to analyzing specific piecesof machinery and assets. These existing methods could be imported intothe configured streaming data collectors 102, 4510, 4610, 4710 or thedata processing facility 4860 as portable algorithms or methodologies.Data processing, such as described herein for the configured streamingdata collectors 102, 4510, 4610, 4710 may also match an algorithm ormethodology to a situation, then extract data from a stream to match tothe data methodology from the legacy acquisition or legacy acquisitiontechniques. In embodiments, the streaming data collectors 102, 4510,4610, 4710 may be compatible with many types of systems and may becompatible with systems having varying degrees of criticality.

Exemplary industrial machine deployments of the methods and systemsdescribed herein are now described. An industrial machine may be a gascompressor. In an example, a gas compressor may operate an oil pump on avery large turbo machine, such as a very large turbo machine thatincludes 10,000 HP motors. The oil pump may be a highly critical systemas its failure could cause an entire plant to shut down. The gascompressor in this example may run four stages at a very high frequency,such as 36,000 RPM, and may include tilt pad bearings that ride on anoil film. The oil pump in this example may have roller bearings, suchthat if an anticipated failure is not being picked up by a user, the oilpump may stop running, and the entire turbo machine would fail.Continuing with this example, the streaming data collectors 102, 4510,4610, 4710 may collect data related to vibrations, such as casingvibration and proximity probe vibration. Other bearings industrialmachine examples may include generators, power plants, boiler feedpumps, fans, forced draft fans, induced draft fans, and the like. Thestreaming data collectors 102, 4510, 4610, 4710 for a bearings systemused in the industrial gas industry may support predictive analysis onthe motors, such as that performed by model-based expert systems—forexample, using voltage, current, and vibration as analysis metrics.

Another exemplary industrial machine deployment may be a motor and thestreaming data collectors 102, 4510, 4610, 4710 that may assist in theanalysis of a motor by collecting voltage and current data on the motor,for example.

Yet another exemplary industrial machine deployment may include oilquality sensing. An industrial machine may conduct oil analysis, and thestreaming data collectors 102, 4510, 4610, 4710 may assist in searchingfor fragments of metal in oil, for example.

The methods and systems described herein may also be used in combinationwith model-based systems. Model-based systems may integrate withproximity probes. Proximity probes may be used to sense problems withmachinery and shut machinery down due to sensed problems. A model-basedsystem integrated with proximity probes may measure a peak waveform andsend a signal that shuts down machinery based on the peak waveformmeasurement.

Enterprises that operate industrial machines may operate in many diverseindustries. These industries may include industries that operatemanufacturing lines, provide computing infrastructure, support financialservices, provide HVAC equipment, and the like. These industries may behighly sensitive to lost operating time and the cost incurred due tolost operating time. HVAC equipment enterprises in particular may beconcerned with data related to ultrasound, vibration, IR, and the like,and may get much more information about machine performance related tothese metrics using the methods and systems of industrial machine senseddata streaming collection than from legacy systems.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for capturing a plurality ofstreams of sensed data from sensors deployed to monitor aspects of anindustrial machine associated with at least one moving part of themachine; at least one of the streams containing a plurality offrequencies of data. The method may include identifying a subset of datain at least one of the multiple streams that corresponds to datarepresenting at least one predefined frequency. The at least onepredefined frequency is represented by a set of data collected fromalternate sensors deployed to monitor aspects of the industrial machineassociated with the at least one moving part of the machine. The methodmay further include processing the identified data with a dataprocessing facility that processes the identified data with datamethodologies configured to be applied to the set of data collected fromalternate sensors. Lastly, the method may include storing the at leastone of the streams of data, the identified subset of data, and a resultof processing the identified data in an electronic data set.

The methods and systems may include a method for applying data capturedfrom sensors deployed to monitor aspects of an industrial machineassociated with at least one moving part of the machine, the datacaptured with predefined lines of resolution covering a predefinedfrequency range, to a frequency matching facility that identifies asubset of data streamed from other sensors deployed to monitor aspectsof the industrial machine associated with at least one moving part ofthe machine, the streamed data comprising a plurality of lines ofresolution and frequency ranges, the subset of data identifiedcorresponding to the lines of resolution and predefined frequency range.This method may include storing the subset of data in an electronic datarecord in a format that corresponds to a format of the data capturedwith predefined lines of resolution, and signaling to a data processingfacility the presence of the stored subset of data. This method mayoptionally include processing the subset of data with at least one ofalgorithms, methodologies, models, and pattern recognizers thatcorresponds to algorithms, methodologies, models, and patternrecognizers associated with processing the data captured with predefinedlines of resolution covering a predefined frequency range.

The methods and systems may include a method for identifying a subset ofstreamed sensor data. The sensor data is captured from sensors deployedto monitor aspects of an industrial machine associated with at least onemoving part of the machine. The subset of streamed sensor data is atpredefined lines of resolution for a predefined frequency range. Themethod includes establishing a first logical route for communicatingelectronically between a first computing facility performing theidentifying and a second computing facility. The identified subset ofthe streamed sensor data is communicated exclusively over theestablished first logical route when communicating the subset ofstreamed sensor data from the first facility to the second facility.This method may further include establishing a second logical route forcommunicating electronically between the first computing facility andthe second computing facility for at least one portion of the streamedsensor data that is not the identified subset. This method may furtherinclude establishing a third logical route for communicatingelectronically between the first computing facility and the secondcomputing facility for at least one portion of the streamed sensor datathat includes the identified subset and at least one other portion ofthe data not represented by the identified subset.

The methods and systems may include a first data sensing and processingsystem that captures first data from a first set of sensors deployed tomonitor aspects of an industrial machine associated with at least onemoving part of the machine, the first data covering a set of lines ofresolution and a frequency range. This system may include a second datasensing and processing system that captures and streams a second set ofdata from a second set of sensors deployed to monitor aspects of theindustrial machine associated with at least one moving part of themachine, the second data covering a plurality of lines of resolutionthat includes the set of lines of resolution and a plurality offrequencies that includes the frequency range. The system may enable:(1) selecting a portion of the second data that corresponds to the setof lines of resolution and the frequency range of the first data; and(2) processing the selected portion of the second data with the firstdata sensing and processing system.

The methods and systems may include a method for automaticallyprocessing a portion of a stream of sensed data. The sensed datareceived from a first set of sensors deployed to monitor aspects of anindustrial machine associated with at least one moving part of themachine in response to an electronic data structure that facilitatesextracting a subset of the stream of sensed data that corresponds to aset of sensed data received from a second set of sensors deployed tomonitor the aspects of the industrial machine associated with the atleast one moving part of the machine. The set of sensed data isconstrained to a frequency range. The stream of sensed data includes arange of frequencies that exceeds the frequency range of the set ofsensed data. The processing comprises executing data methodologies on aportion of the stream of sensed data that is constrained to thefrequency range of the set of sensed data. The data methodologies areconfigured to process the set of sensed data.

The methods and systems may include a method for receiving first datafrom sensors deployed to monitor aspects of an industrial machineassociated with at least one moving part of the machine. This method mayfurther include: (1) detecting at least one of a frequency range andlines of resolution represented by the first data, and (2) receiving astream of data from sensors deployed to monitor the aspects of theindustrial machine associated with the at least one moving part of themachine. The stream of data includes: a plurality of frequency rangesand a plurality of lines of resolution that exceeds the frequency rangeand the lines of resolution represented by the first data; extracting aset of data from the stream of data that corresponds to at least one ofthe frequency range and the lines of resolution represented by the firstdata; and processing the extracted set of data with a data processingmethod that is configured to process data within the frequency range andwithin the lines of resolution of the first data.

The methods and systems disclosed herein may include, connect to, or beintegrated with a data acquisition instrument and in the manyembodiments, FIG. 22 shows methods and systems 5000 that includes a dataacquisition (DAQ) streaming instrument 5002 also known as an SDAQ. Inembodiments, output from sensors 5010, 5012, 5014 may be of varioustypes including vibration, temperature, pressure, ultrasound and so on.In my many examples, one of the sensors may be used. In furtherexamples, many of the sensors may be used and their signals may be usedindividually or in predetermined combinations and/or at predeterminedintervals, circumstances, setups, and the like.

In embodiments, the output signals from the sensors 5010, 5012, 5014 maybe fed into instrument inputs 5020, 5022, 5024 of the DAQ instrument5002 and may be configured with additional streaming capabilities 5028.By way of these many examples, the output signals from the sensors 5010,5012, 5014, or more as applicable, may be conditioned as an analogsignal before digitization with respect to at least scaling andfiltering. The signals may then be digitized by an analog-to-digitalconverter 5030. The signals received from all relevant channels (i.e.,one or more channels are switched on manually, by alarm, by route, andthe like) may be simultaneously sampled at a predetermined ratesufficient to perform the maximum desired frequency analysis that may beadjusted and readjusted as needed or otherwise held constant to ensurecompatibility or conformance with other relevant datasets. Inembodiments, the signals are sampled for a relatively long time andgap-free as one continuous stream so as to enable furtherpost-processing at lower sampling rates with sufficient individualsampling.

In embodiments, data may be streamed from a collection of points andthen the next set of data may be collected from additional pointsaccording to a prescribed sequence, route, path, or the like. In manyexamples, the sensors 5010, 5012, 5014 or more may be moved to the nextlocation according to the prescribed sequence, route, pre-arrangedconfigurations, or the like. In certain examples, not all of the sensor5010, 5012, 5014 may move and therefore some may remain fixed in placeand used for detection of reference phase or the like.

In embodiments, a multiplex (mux) 5032 may be used to switch to the nextcollection of points, to a mixture of the two methods or collectionpatterns that may be combined, other predetermined routes, and the like.The multiplexer 5032 may be stackable so as to be laddered andeffectively accept more channels than the DAQ instrument 5002 provides.In examples, the DAQ instrument 5002 may provide eight channels whilethe multiplexer 5032 may be stacked to supply 32 channels. Furthervariations are possible with one more multiplexers. In embodiments, themultiplexer 5032 may be fed into the DAQ instrument 5002 through aninstrument input 5034. In embodiments, the DAQ instrument 5002 mayinclude a controller 5038 that may take the form of an onboardcontroller, a PC, other connected devices, network based services, andcombinations thereof.

In embodiments, the sequence and panel conditions used to govern thedata collection process may be obtained from the multimedia probe (MMP)and probe control, sequence and analytical (PCSA) information store5040. In embodiments, the information store 5040 may be onboard the DAQinstrument 5002. In embodiments, contents of the information store 5040may be obtained through a cloud network facility, from other DAQinstruments, from other connected devices, from the machine beingsensed, other relevant sources, and combinations thereof. Inembodiments, the information store 5040 may include such items as thehierarchical structural relationships of the machine, e.g., a machinecontains predetermined pieces of equipment, each of which may containone or more shafts and each of those shafts may have multiple associatedbearings. Each of those types of bearings may be monitored by specifictypes of transducers or probes, according to one or more specificprescribed sequences (paths, routes, and the like) and with one or morespecific panel conditions that may be set on the one or more DAQinstruments 5002. By way of this example, the panel conditions mayinclude hardware specific switch settings or other collectionparameters. In many examples, collection parameters include but are notlimited to a sampling rate, AC/DC coupling, voltage range and gain,integration, high and low pass filtering, anti-aliasing filtering, ICP™transducers and other integrated-circuit piezoelectric transducers, 4-20mA loop sensors, and the like. In embodiments, the information store5040 may also include machinery specific features that may be importantfor proper analysis such as gear teeth for a gear, number blades in apump impeller, number of motor rotor bars, bearing specific parametersnecessary for calculating bearing frequencies, revolution per minutesinformation of all rotating elements and multiples of those RPM ranges,and the like. Information in the information store may also be used toextract stream data 5050 for permanent storage.

Based on directions from a DAQ API software 5052, digitized waveformsmay be uploaded using a DAQ driver services 5054 of a driver onboard theDAQ instrument 5002. In embodiments, data may then be fed into a rawdata server 5058 which may store the stream data 5050 in a stream datarepository 5060. In embodiments, this data storage area is typicallymeant for storage until the data is copied off of the DAQ instrument5002 and verified. The DAQ API 5052 may also direct a local data controlapplication 5062 to extract and process the recently obtained streamdata 5050 and convert it to the same or lower sampling rates ofsufficient length to effect one or more desired resolutions. By way ofthese examples, this data may be converted to spectra, averaged, andprocessed in a variety of ways and stored, at least temporarily, asextracted/processed (EP) data 5064. It will be appreciated in light ofthe disclosure that legacy data may require its own sampling rates andresolution to ensure compatibility and often this sampling rate may notbe integer proportional to the acquired sampling rate. It will also beappreciated in light of the disclosure that this may be especiallyrelevant for order-sampled data whose sampling frequency is relateddirectly to an external frequency (typically the running speed of themachine or its local componentry) rather than the more-standard samplingrates employed by the internal crystals, clock functions, or the like ofthe DAQ instrument (e.g., values of Fmax of 100, 200, 500, 1K, 2K, 5K,10K, 20K, and so on).

In embodiments, an extract/process (EP) align module 5068 of the localdata control application 5062 may be able to fractionally adjust thesampling rates to these non-integer ratio rates satisfying an importantrequirement for making data compatible with legacy systems. Inembodiments, fractional rates may also be converted to integer ratiorates more readily because the length of the data to be processed may beadjustable. It will be appreciated in light of the disclosure that ifthe data was not streamed and just stored as spectra with the standardor predetermined Fmax, it may be impossible in certain situations toconvert it retroactively and accurately to the order-sampled data. Itwill also be appreciated in light of the disclosure that internalidentification issues may also need to be reconciled. In many examples,stream data may be converted to the proper sampling rate and resolutionas described and stored (albeit temporarily) in an EP legacy datarepository 5070 to ensure compatibility with legacy data.

To support legacy data identification issues, a user input module 5072is shown in many embodiments should there be no automated process(whether partially or wholly) for identification translation. In suchexamples, one or more legacy systems (i.e., pre-existing dataacquisition) may be characterized in that the data to be imported is ina fully standardized format such as a Mimosa™ format, and other similarformats. Moreover, sufficient indentation of the legacy data and/or theone or more machines from which the legacy data was produced may berequired in the completion of an identification mapping table 5074 toassociate and link a portion of the legacy data to a portion of thenewly acquired streamed data 5050. In many examples, the end user and/orlegacy vendor may be able to supply sufficient information to completeat least a portion of a functioning identification (ID) mapping table5074 and therefore may provide the necessary database schema for the rawdata of the legacy system to be used for comparison, analysis, andmanipulation of the newly streamed data 5050.

In embodiments, the local data control application 5062 may also directstreaming data as well as extracted/processed (EP) data to a cloudnetwork facility 5080 via wired or wireless transmission. From the cloudnetwork facility 5080 other devices may access, receive, and maintaindata including the data from a master raw data server (MRDS) 5082. Themovement, distribution, storage, and retrieval of data remote to the DAQinstrument 5002 may be coordinated by a cloud data management services(“CDMS”) 5084.

FIG. 23 shows additional methods and systems that include the DAQinstrument 5002 accessing related cloud based services. In embodiments,the DAQ API 5052 may control the data collection process as well as itssequence. By way of these examples, the DAQ API 5052 may provide thecapability for editing processes, viewing plots of the data, controllingthe processing of that data, viewing the output data in all its myriadforms, analyzing this data including expert analysis, and communicatingwith external devices via the local data control application 5062 andwith the CDMS 5084 via the cloud network facility 5080. In embodiments,the DAQ API 5052 may also govern the movement of data, its filtering, aswell as many other housekeeping functions.

In embodiments, an expert analysis module 5100 may generate reports 5102that may use machine or measurement point specific information from theinformation store 5040 to analyze the stream data 5050 using a streamdata analyzer module 5104 and the local data control application 5062with the extract/process (“EP”) align module 5068. In embodiments, theexpert analysis module 5100 may generate new alarms or ingest alarmsettings into an alarms module 5108 that is relevant to the stream data5050. In embodiments, the stream data analyzer module 5104 may provide amanual or automated mechanism for extracting meaningful information fromthe stream data 5050 in a variety of plotting and report formats. Inembodiments, a supervisory control of the expert analysis module 5100 isprovided by the DAQ API 5052. In further examples, the expert analysismodule 5100 may be supplied (wholly or partially) via the cloud networkfacility 5080. In many examples, the expert analysis module 5100 via thecloud may be used rather than a locally-deployed expert analysis module5100 for various reasons such as using the most up-to-date softwareversion, more processing capability, a bigger volume of historical datato reference, and so on. In many examples, it may be important that theexpert analysis module 5100 be available when an internet connectioncannot be established so having this redundancy may be crucial forseamless and time efficient operation. Toward that end, many of themodular software applications and databases available to the DAQinstrument 5002 where applicable may be implemented with systemcomponent redundancy to provide operational robustness to provideconnectivity to cloud services when needed but also operate successfullyin isolated scenarios where connectivity is not available and sometimenot available purposefully to increase security and the like.

In embodiments, the DAQ instrument acquisition may require a real timeoperating system (“RTOS”) for the hardware especially for streamedgap-free data that is acquired by a PC. In some instances, therequirement for a RTOS may result in (or may require) expensive customhardware and software capable of running such a system. In manyembodiments, such expensive custom hardware and software may be avoidedand an RTOS may be effectively and sufficiently implemented using astandard Windows™ operating systems or similar environments includingthe system interrupts in the procedural flow of a dedicated applicationincluded in such operating systems.

The methods and systems disclosed herein may include, connect to, or beintegrated with one or more DAQ instruments and in the many embodiments,FIG. 24 shows methods and systems 5150 that include the DAQ instrument5002 (also known as a streaming DAQ or an SDAQ). In embodiments, the DAQinstrument 5002 may effectively and sufficiently implement an RTOS usingstandard windows operating system (or other similar personal computingsystems) that may include a software driver configured with a First In,First Out (FIFO) memory area 5152. The FIFO memory area 5152 may bemaintained and hold information for a sufficient amount of time tohandle a worst-case interrupt that it may face from the local operatingsystem to effectively provide the RTOS. In many examples, configurationson a local personal computer or connected device may be maintained tominimize operating system interrupts. To support this, theconfigurations may be maintained, controlled, or adjusted to eliminate(or be isolated from) any exposure to extreme environments whereoperating system interrupts may become an issue. In embodiments, the DAQinstrument 5002 may produce a notification, alarm, message, or the liketo notify a user when any gap errors are detected. In these manyexamples, such errors may be shown to be rare and even if they occur,the data may be adjusted knowing when they occurred should such asituation arise.

In embodiments, the DAQ instrument 5002 may maintain a sufficientlylarge FIFO memory area 5152 that may buffer the incoming data so as tobe not affected by operating system interrupts when acquiring data. Itwill be appreciated in light of the disclosure that the predeterminedsize of the FIFO memory area 5152 may be based on operating systeminterrupts that may include Windows system and application functionssuch as the writing of data to Disk or SSD, plotting, GUI interactionsand standard Windows tasks, low-level driver tasks such as servicing theDAQ hardware and retrieving the data in bursts, and the like.

In embodiments, the computer, controller, connected device or the likethat may be included in the DAQ instrument 5002 may be configured toacquire data from the one or more hardware devices over a USB port,firewire, ethernet, or the like. In embodiments, the DAQ driver services5054 may be configured to have data delivered to it periodically so asto facilitate providing a channel specific FIFO memory buffer that maybe configured to not miss data, i.e., it is gap-free. In embodiments,the DAQ driver services 5054 may be configured so as to maintain an evenlarger (than the device) channel specific FIFO area 5152 that it fillswith new data obtained from the device. In embodiments, the DAQ driverservices 5054 may be configured to employ a further process in that theraw data server 5058 may take data from a FIFO 5110 and may write it asa contiguous and continuous stream to non-volatile storage areas such asthe stream data repository 5060 that may be configured as one or moredisk drives, SSDs, or the like. In embodiments, the FIFO 5110 may beconfigured to include a starting and stopping marker or pointer to markwhere the latest most current stream was written. By way of theseexamples, a FIFO end marker 5114 may be configured to mark the end ofthe most current data until it reaches the end of the spooler and thenwraps around constantly cycling around. In these examples, there isalways one megabyte (or other configured capacities) of the most currentdata available in the FIFO 5110 once the spooler fills up. It will beappreciated in light of the disclosure that further configurations ofthe FIFO memory area may be employed. In embodiments, the DAQ driverservices 5054 may be configured to use the DAQ API 5052 to pipe the mostrecent data to a high-level application for processing, graphing andanalysis purposes. In some examples, it is not required that this databe gap-free but even in these instances, it is helpful to identify andmark the gaps in the data. Moreover, these data updates may beconfigured to be frequent enough so that the user would perceive thedata as live. In the many embodiments, the raw data is flushed tonon-volatile storage without a gap at least for the prescribed amount oftime and examples of the prescribed amount of time may be about thirtyseconds to over four hours. It will be appreciated in light of thedisclosure that many pieces of equipment and their components maycontribute to the relative needed duration of the stream of gap-freedata and those durations may be over four hours when relatively lowspeeds are present in large numbers, when non-periodic transientactivity is occurring on a relatively long time frame, when duty cycleonly permits operation in relevant ranges for restricted durations andthe like.

With reference to FIG. 23, the stream data analyzer module 5104 mayprovide for the manual or extraction of information from the data streamin a variety of plotting and report formats. In embodiments, resampling,filtering (including anti-aliasing), transfer functions, spectrumanalysis, enveloping, averaging, peak detection functionality, as wellas a host of other signal processing tools, may be available for theanalyst to analyze the stream data and to generate a very large array ofsnapshots. It will be appreciated in light of the disclosure that muchlarger arrays of snapshots are created than ever would have beenpossible by scheduling the collection of snapshots beforehand, i.e.,during the initial data acquisition for the measurement point inquestion.

FIG. 25 depicts a display 5200 whose viewable content 5202 may beaccessed locally or remotely, wholly or partially. In many embodiments,the display 5200 may be part of the DAQ instrument 5002, may be part ofthe PC or connected device 5038 that may be part of the DAQ instrument5002, or its viewable content 5202 may be viewable from associatednetwork connected displays. In further examples, the viewable content5202 of the display 5200 or portions thereof may be ported to one ormore relevant network addresses. In the many embodiments, the viewablecontent 5202 may include a screen 5204 that shows, for example, anapproximately two-minute data stream 5208 may be collected at a samplingrate of 25.6 kHz for four channels 5220, 5222, 5224, 5228,simultaneously. By way of these examples and in these configurations,the length of the data may be approximately 3.1 megabytes. It will beappreciated in light of the disclosure that the data stream (includingeach of its four channels or as many as applicable) may be replayed insome aspects like a magnetic tape recording (e.g., a reel-to-reel or acassette) with all of the controls normally associated with playbacksuch as forward 5230, fast forward, backward 5232, fast rewind, stepback, step forward, advance to time point, retreat to time point,beginning 5234, end, 5238, play 5240, stop 5242, and the like.Additionally, the playback of the data stream may further be configuredto set a width of the data stream to be shown as a contiguous subset ofthe entire stream. In the example with a two-minute data stream, theentire two minutes may be selected by the “select all” button 5244, orsome subset thereof may be selected with the controls on the screen 5204or that may be placed on the screen 5204 by configuring the display 5200and the DAQ instrument 5002. In this example, the “process selecteddata” button 5250 on the screen 5204 may be selected to commit to aselection of the data stream.

FIG. 26 depicts the many embodiments that include a screen 5250 on thedisplay 5200 that shows results of selecting all of the data for thisexample. In embodiments, the screen 5250 in FIG. 26 may provide the sameor similar playback capabilities as what is depicted on the screen 5204shown in FIG. 25 but also includes resampling capabilities, waveformdisplays, and spectrum displays. In light of the disclosure, it will beappreciated that this functionality may permit the user to choose inmany situations any Fmax less than that supported by the originalstreaming sampling rate. In embodiments, any section of any size may beselected and further processing, analytics, and tools for viewing anddissecting the data may be provided. In embodiments, the screen 5250 mayinclude four windows 5252, 5254, 5258, 5260 that show the stream datafrom the four channels 5220, 5222, 5224, 5228 of FIG. 25. Inembodiments, the screen 5250 may also include offset and overlapcontrols 5262, resampling controls 5264, and other similar controls.

In many examples, any one of many transfer functions may be establishedbetween any two channels, such as the two channels 5280, 5282 that maybe shown on a screen 5284, shown on the display 5200, as depicted inFIG. 27. The selection of the two channels 5280, 5282 on the screen 5284may permit the user to depict the output of the transfer function on anyof the screens including screen 5284 and screen 5204.

In embodiments, FIG. 28 shows a high-resolution spectrum screen 5300 onthe display 5200 with a waveform view 5302, full cursor control 5304 anda peak extraction view 5308. In these examples, the peak extraction view5308 may be configured with a resolved configuration 5310 that may beconfigured to provide enhanced amplitude and frequency accuracy and mayuse spectral sideband energy distribution. The peak extraction view 5308may also be configured with averaging 5312, phase and cursor vectorinformation 5314, and the like.

In embodiments, FIG. 29 shows an enveloping screen 5350 on the display5200 with a waveform view 5352, and a spectral format view 5354. Theviews 5352, 5354 on the enveloping screen 5350 may display modulationfrom the signal in both waveform and spectral formats. In embodiments,FIG. 30 shows a relative phase screen 5380 on the display 5200 with fourphase views 5382, 5384, 5388, 5390. The four phase views 5382, 5384,5388, 5390 relate to the on spectrum the enveloping screen 5350 that maydisplay modulation from the signal in waveform format in the view 5352and spectral format in the view 5354. In embodiments, the referencechannel control 5392 may be selected to use channel four as a referencechannel to determine relative phase between each of the channels.

It will be appreciated in light of the disclosure that the samplingrates of vibration data of up to 100 kHz (or higher in some scenarios)may be utilized for non-vibration sensors as well. In doing so, it willfurther be appreciated in light of the disclosure that stream data insuch durations at these sampling rates may uncover new patterns to beanalyzed due in no small part that many of these types of sensors havenot been utilized in this manner. It will also be appreciated in lightof the disclosure that different sensors used in machinery conditionmonitoring may provide measurements more akin to static levels ratherthan fast-acting dynamic signals. In some cases, faster response timetransducers may have to be used prior to achieving the faster samplingrates.

In many embodiments, sensors may have a relatively static output such astemperature, pressure, or flow but may still be analyzed with thedynamic signal processing system and methodologies as disclosed herein.It will be appreciated in light of the disclosure that the time scale,in many examples, may be slowed down. In many examples, a collection oftemperature readings collected approximately every minute for over twoweeks may be analyzed for their variation solely or in collaboration orin fusion with other relevant sensors. By way of these examples, thedirect current level or average level may be omitted from all thereadings (e.g., by subtraction) and the resulting delta measurements maybe processed (e.g., through a Fourier transform). From these examples,resulting spectral lines may correlate to specific machinery behavior orother symptoms present in industrial system processes. In furtherexamples, other techniques include enveloping that may look formodulation, wavelets that may look for spectral patterns that last onlyfor a short time (e.g., bursts), cross-channel analysis to look forcorrelations with other sensors including vibration, and the like.

FIG. 31 shows a DAQ instrument 5400 that may be integrated with one ormore analog sensors 5402 and endpoint nodes 5404 to provide a streamingsensor 5410 or smart sensors that may take in analog signals and thenprocess and digitize them, and then transmit them to one or moreexternal monitoring systems 5412 in the many embodiments that may beconnected to, interfacing with, or integrated with the methods andsystems disclosed herein. The monitoring system 5412 may include astreaming hub server 5420 that may communicate with the CDMS 5084. Inembodiments, the CDMS 5084 may contact, use, and integrate with clouddata 5430 and cloud services 5432 that may be accessible through one ormore cloud network facilities 5080. In embodiments, the streaming hubserver 5420 may connect with another streaming sensor 5440 that mayinclude a DAQ instrument 5442, an endpoint node 5444, and the one ormore analog sensors such as analog sensor 5448. The steaming hub server5420 may connect with other streaming sensors such as a streaming sensor5460 that may include a DAQ instrument 5462, an endpoint node 5464, andthe one or more analog sensors such as analog sensor 5468.

In embodiments, there may be additional streaming hub servers such asthe steaming hub server 5480 that may connect with other streamingsensors such as the streaming sensor 5490 that may include a DAQinstrument 5492, an endpoint node 5494, and the one or more analogsensors such as analog sensor 5498. In embodiments, the streaming hubserver 5480 may also connect with other streaming sensors such as thestreaming sensor 5500 that may include a DAQ instrument 5502, anendpoint node 5504, and the one or more analog sensors such as analogsensor 5508. In embodiments, the transmission may include averagedoverall levels and in other examples may include dynamic signal sampledat a prescribed and/or fixed rate. In embodiments, the streaming sensors5410, 5440, 5460, 5490, and 5500 may be configured to acquire analogsignals and then apply signal conditioning to those analog signalsincluding coupling, averaging, integrating, differentiating, scaling,filtering of various kinds, and the like. The streaming sensors 5410,5440, 5460, 5490, and 5500 may be configured to digitize the analogsignals at an acceptable rate and resolution (number of bits) and toprocess further the digitized signal when required. The streamingsensors 5410, 5440, 5460, 5490, and 5500 may be configured to transmitthe digitized signals at pre-determined, adjustable, and re-adjustablerates. In embodiments, the streaming sensors 5410, 5440, 5460, 5490, and5500 are configured to acquire, digitize, process, and transmit data ata sufficient effective rate so that a relatively consistent stream ofdata may be maintained for a suitable amount of time so that a largenumber of effective analyses may be shown to be possible. In the manyembodiments, there would be no gaps in the data stream and the length ofdata should be relatively long, ideally for an unlimited amount of time,although practical considerations typically require ending the stream.It will be appreciated in light of the disclosure that this longduration data stream with effectively no gap in the stream is incontrast to the more commonly used burst collection where data iscollected for a relatively short period of time (i.e., a short burst ofcollection), followed by a pause, and then perhaps another burstcollection and so on. In the commonly used collections of data collectedover noncontiguous bursts, data would be collected at a slow rate forlow frequency analysis and high frequency for high frequency analysis.In many embodiments of the present disclosure, in contrast, thestreaming data is being collected (i) once, (ii) at the highest usefuland possible sampling rate, and (iii) for a long enough time that lowfrequency analysis may be performed as well as high frequency. Tofacilitate the collection of the streaming data, enough storage memorymust be available on the one or more streaming sensors such as thestreaming sensors 5410, 5440, 5460, 5490, 5500 so that new data may beoff-loaded externally to another system before the memory overflows. Inembodiments, data in this memory would be stored into and accessed from“First-In, First-Out” (“FIFO”) mode. In these examples, the memory witha FIFO area may be a dual port so that the sensor controller may writeto one part of it while the external system reads from a different part.In embodiments, data flow traffic may be managed by semaphore logic.

It will be appreciated in light of the disclosure that vibrationtransducers that are larger in mass will have a lower linear frequencyresponse range because the natural resonance of the probe is inverselyrelated to the square root of the mass and will be lowered. Toward thatend, a resonant response is inherently non-linear and so a transducerwith a lower natural frequency will have a narrower linear passbandfrequency response. It will also be appreciated in light of thedisclosure that above the natural frequency the amplitude response ofthe sensor will taper off to negligible levels rendering it even moreunusable. With that in mind, high frequency accelerometers, for thisreason, tend to be quite small in mass, to the order of half of a gram.It will also be appreciated in light of the disclosure that adding therequired signal processing and digitizing electronics required forstreaming may, in certain situations, render the sensors incapable inmany instances of measuring high-frequency activity.

In embodiments, streaming hubs such as the streaming hubs 5420, 5480 mayeffectively move the electronics required for streaming to an externalhub via cable. It will be appreciated in light of the disclosure thatthe streaming hubs may be located virtually next to the streamingsensors or up to a distance supported by the electronic drivingcapability of the hub. In instances where an internet cache protocol(“ICP”) is used, the distance supported by the electronic drivingcapability of the hub would be anywhere from 100 to 1000 feet (30.5 to305 meters) based on desired frequency response, cable capacitance, andthe like. In embodiments, the streaming hubs may be positioned in alocation convenient for receiving power as well as connecting to anetwork (be it LAN or WAN). In embodiments, other power options wouldinclude solar, thermal as well as energy harvesting. Transfer betweenthe streaming sensors and any external systems may be wireless or wiredand may include such standard communication technologies as 802.11 and900 MHz wireless systems, Ethernet, USB, firewire and so on.

With reference to FIG. 22, the many examples of the DAQ instrument 5002include embodiments where data that may be uploaded from the local datacontrol application 5062 to the master raw data server (“MRDS”) 5082. Inembodiments, information in the multimedia probe (“MMP”) and probecontrol, sequence and analytical (“PCSA”) information store 5040 mayalso be downloaded from the MRDS 5082 down to the DAQ instrument 5002.Further details of the MRDS 5082 are shown in FIG. 32 includingembodiments where data may be transferred to the MRDS 5082 from the DAQinstrument 5002 via a wired or wireless network, or through connectionto one or more portable media, drive, other network connections, or thelike. In embodiments, the DAQ instrument 5002 may be configured to beportable and may be carried on one or more predetermined routes toassess predefined points of measurement. In these many examples, theoperating system that may be included in the MRDS 5082 may be Windows™Linux™, or MacOS™ operating systems, or other similar operating systems.Further, in these arrangements, the operating system, modules for theoperating system, and other needed libraries, data storage, and the likemay be accessible wholly or partially through access to the cloudnetwork facility 5080. In embodiments, the MRDS 5082 may reside directlyon the DAQ instrument 5002, especially in on-line system examples. Inembodiments, the DAQ instrument 5002 may be linked on an intra-networkin a facility but may otherwise be behind a firewall. In furtherexamples, the DAQ instrument 5002 may be linked to the cloud networkfacility 5080. In the various embodiments, one of the computers ormobile computing devices may be effectively designated the MRDS 5082 towhich all of the other computing devices may feed it data such as one ofthe MRDS 6104, as depicted in FIGS. 41 and 42. In the many exampleswhere the DAQ instrument 5002 may be deployed and configured to receivestream data in a swarm environment, one or more of the DAQ instruments5002 may be effectively designated the MRDS 5082 to which all of theother computing devices may feed it data. In the many examples where theDAQ instrument 5002 may be deployed and configured to receive streamdata in an environment where the methods and systems disclosed hereinare intelligently assigning, controlling, adjusting, and re-adjustingdata pools, computing resources, network bandwidth for local datacollection, and the like, one or more of the DAQ instruments 5002 may beeffectively designated the MRDS 5082 to which all of the other computingdevices may feed it data.

With further reference to FIG. 32, new raw streaming data, data thathave been through extract, process, and align processes (EP data), andthe like may be uploaded to one or more master raw data servers asneeded or as scaled in various environments. In embodiments, a masterraw data server (“MRDS”) 5700 may connect to and receive data from othermaster raw data servers such as the MRDS 5082. The MRDS 5700 may includea data distribution manager module 5702. In embodiments, the new rawstreaming data may be stored in a new stream data repository 5704. Inmany instances, like raw data streams stored on the DAQ instrument 5002,the new stream data repository 5704 and new extract and process datarepository 5708 may be similarly configured as a temporary storage area.

In embodiments, the MRDS 5700 may include a stream data analyzer modulewith an extract and process alignment module. The analyzer module 5710may be shown to be a more robust data analyzer and extractor than may betypically found on portable streaming DAQ instruments although it may bedeployed on the DAQ instrument 5002 as well. In embodiments, theanalyzer module 5710 takes streaming data and instantiates it at aspecific sampling rate and resolution similar to the local data controlmodule 5062 on the DAQ instrument 5002. The specific sampling rate andresolution of the analyzer module 5710 may be based on either a userinput 5712 or automated extractions from a multimedia probe (“MMP”) andthe probe control, sequence and analytical (“PCSA”) information store5714 and/or an identification mapping table 5718, which may require theuser input 5712 if there is incomplete information regarding variousforms of legacy data similar to as was detailed with the DAQ instrument5002. In embodiments, legacy data may be processed with the analyzermodule 5710 and may be stored in one or more temporary holding areassuch as a new legacy data repository 5720. One or more temporary areasmay be configured to hold data until it is copied to an archive andverified. The analyzer 5710 module may also facilitate in-depth analysisby providing many varying types of signal processing tools including butnot limited to filtering, Fourier transforms, weighting, resampling,envelope demodulation, wavelets, two-channel analysis, and the like.From this analysis, many different types of plots and mini-reports maybe generated from a reports and plots module 5724. In embodiments, datais sent to the processing, analysis, reports, and an archiving (“PARA”)server 5730 upon user initiation or in an automated fashion especiallyfor on-line systems.

In embodiments, a PARA server 5750 may connect to and receive data fromother PARA servers such as the PARA server 5730. With reference to FIG.34, the PARA server 5730 may provide data to a supervisory module 5752on the PARA server 5750 that may be configured to provide at least oneof processing, analysis, reporting, archiving, supervisory, and similarfunctionalities. The supervisory module 5752 may also contain extract,process align functionality and the like. In embodiments, incomingstreaming data may first be stored in a raw data stream archive 5760after being properly validated. Based on the analytical requirementsderived from a multimedia probe (“MMP”) and probe control, sequence andanalytical (“PCSA”) information store 5762 as well as user settings,data may be extracted, analyzed, and stored in an extract and process(“EP”) raw data archive 5764. In embodiments, various reports from areports module 5768 are generated from the supervisory module 5752. Thevarious reports from the reports module 5768 include trend plots ofvarious smart bands, overalls along with statistical patterns, and thelike. In embodiments, the reports module 5768 may also be configured tocompare incoming data to historical data. By way of these examples, thereports module 5768 may search for and analyze adverse trends, suddenchanges, machinery defect patterns, and the like. In embodiments, thePARA server 5750 may include an expert analysis module 5770 from whichreports are generated and analysis may be conducted. Upon completion,archived data may be fed to a local master server (“LMS”) 5772 via aserver module 5774 that may connect to the local area network. Inembodiments, archived data may also be fed to the LMS 5772 via a clouddata management server (“CDMS”) 5778 through a server module for thecloud network facility 5080. In embodiments, the supervisory module 5752on the PARA server 5750 may be configured to provide at least one ofprocessing, analysis, reporting, archiving, supervisory, and similarfunctionalities from which alarms may be generated, rated, stored,modified, reassigned, and the like with an alarm generator module 5782.

FIG. 34 depicts various embodiments that include a PARA server and itsconnection to a LAN 5802. In embodiments, one or more DAQ instrumentssuch as the DAQ instrument 5002 may receive and process analog data fromone or more analog sensors 5710 that may be fed into the DAQ instrument5002. As discussed herein, the DAQ instrument 5002 may create a digitalstream of data based on the ingested analog data from the one or moreanalog sensors. The digital stream from the DAQ instrument 5002 may beuploaded to the MRDS 5082 and from there, it may be sent to a PARAserver 5800 where multiple terminals, such as terminal 5810 5812, 5814,may each interface with it or the MRDS 5082 and view the data and/oranalysis reports. In embodiments, the PARA server 5800 may communicatewith a network data server 5820 that may include an LMS 5822. In theseexamples, the LMS 5822 may be configured as an optional storage area forarchived data. The LMS 5822 may also be configured as an external driverthat may be connected to a PC or other computing device that may run theLMS 5822; or the LMS 5822 may be directly run by the PARA server 5800where the LMS 5822 may be configured to operate and coexist with thePARA server 5800. The LMS 5822 may connect with a raw data streamarchive 5824, an extract and process (“EP”) raw data archive 5828, andan MMP and probe control, sequence and analytical (“PCSA”) informationstore 5830. In embodiments, a CDMS 5832 may also connect to the LAN 5802and may also support the archiving of data.

In embodiments, a portable connected devices 5850 such as a tablet 5852and a smart phone 5854 may connect the CDMS 5832 using web APIs 5860 and5862, respectively, as depicted in FIG. 35. The APIs 5860, 5862 may beconfigured to execute in a browser and may permit access via a cloudnetwork facility 5870 of all (or some of) the functions previouslydiscussed as accessible through the PARA Server 5800. In embodiments,computing devices of a user 5880 such as computing devices 5882, 5884,5888 may also access the cloud network facility 5870 via a browser orother connection in order to receive the same functionality. Inembodiments, thin-client apps which do not require any other devicedrivers and may be facilitated by web services supported by cloudservices 5890 and cloud data 5892. In many examples, the thin-clientapps may be developed and reconfigured using, for example, the visualhigh-level LabVIEW™ programming language with NXG™ Web-based virtualinterface subroutines. In embodiments, thin client apps may providehigh-level graphing functions such as those supported by LabVIEW™ tools.In embodiments, the LabVIEW™ tools may generate JSCRIPT™ code and JAVA™code that may be edited post-compilation. The NXG™ tools may generateWeb VI's that may not require any specialized driver and only someRESTful™ services which may be readily installed from any browser. Itwill be appreciated in light of the disclosure that because variousapplications may be run inside a browser, the applications may be run onany operating system, such as Windows™, Linux™, and Android™ operatingsystems especially for personal devices, mobile devices, portableconnected devices, and the like.

In embodiments, the CDMS 5832 is depicted in greater detail in FIG. 36.In embodiments, the CDMS 5832 may provide all of the data storage andservices that the PARA Server 5800 (FIG. 34) may provide. In contrast,all of the API's may be web API's which may run in a browser and allother apps may run on the PARA Server 5800 or the DAQ instrument 5002which may typically be Windows™, Linux™ or other similar operatingsystems. In embodiments, the CDMS 5832 includes at least one of orcombinations of the following functions: the CDMS 5832 may include acloud GUI 5900 that may be configured to provide access to all dataplots including trend, waveform, spectra, envelope, transfer function,logs of measurement events, analysis including expert, utilities, andthe like. In embodiments, the CDMS 5832 may include a cloud dataexchange 5902 configured to facilitate the transfer of data to and fromthe cloud network facility 5870. In embodiments, the CDMS 5832 mayinclude a cloud plots/trends module 5904 that may be configured to showall plots via web apps including trend, waveform, spectra, envelope,transfer function, and the like. In embodiments, the CDMS 5832 mayinclude a cloud reporter 5908 that may be configured to provide allanalysis reports, logs, expert analysis, trend plots, statisticalinformation, and the like. In embodiments, the CDMS 5832 may include acloud alarm module 5910. Alarms from the cloud alarm module 5910 may begenerated and may be sent to various devices 5920 via email, texts, orother messaging mechanisms. From the various modules, data may be storedin new data 5914. The various devices 5920 may include a terminal 5922,portable connected device 5924, or a tablet 5928. The alarms from thecloud alarm module are designed to be interactive so that the end usermay acknowledge alarms in order to avoid receiving redundant alarms andalso to see significant context-sensitive data from the alarm pointsthat may include spectra, waveform statistical info, and the like.

In embodiments, a relational database server (“RDS”) 5930 may be used toaccess all of the information from an MMP and PCSA information store5932. As with the PARA server 5800 (FIG. 36), information from theinformation store 5932 may be used with an EP and align module 5934, adata exchange 5938 and an expert system 5940. In embodiments, a raw datastream archive 5942 and extract and process raw data archive 5944 mayalso be used by the EP align 5934, the data exchange 5938 and the expertsystem 5940 as with the PARA server 5800. In embodiments, new stream rawdata 5950, new extract and process raw data 5952, and new data 5954(essentially all other raw data such as overalls, smart bands, stats,and data from the information store 5932) are directed by the CDMS 5832.

In embodiments, the streaming data may be linked with the RDS 5930 andthe MMP and PCSA information store 5932 using a technical datamanagement streaming (“TDMS”) file format. In embodiments, theinformation store 5932 may include tables for recording at leastportions of all measurement events. By way of these examples, ameasurement event may be any single data capture, a stream, a snapshot,an averaged level, or an overall level. Each of the measurement eventsin addition to point identification information may also have a date andtime stamp. In embodiments, a link may be made between the streamingdata, the measurement event, and the tables in the information store5932 using the TDMS format. By way of these examples, the link may becreated by storing unique measurement point identification codes with afile structure having the TDMS format by including and assigning TDMSproperties. In embodiments, a file with the TDMS format may allow forthree levels of hierarchy. By way of these examples, the three levels ofhierarchy may be root, group, and channel. It will be appreciated inlight of the disclosure that the Mimosa™ database schema may be, intheory, unlimited. With that said, there are advantages to limited TDMShierarchies. In the many examples, the following properties may beproposed for adding to the TDMS Stream structure while using a MimosaCompatible database schema.

Root Level: Global ID 1: Text String (This could be a unique ID obtainedfrom the web.); Global ID 2: Text String (This could be an additional IDobtained from the web.); Company Name: Text String; Company ID: TextString; Company Segment ID: 4-byte Integer; Company Segment ID: 4-byteInteger; Site Name: Text String; Site Segment ID: 4-byte Integer; SiteAsset ID: 4-byte Integer; Route Name: Text String; Version Number: TextString

Group Level: Section 1 Name: Text String; Section 1 Segment ID: 4-byteInteger; Section 1 Asset ID: 4-byte Integer; Section 2 Name: TextString; Section 2 Segment ID: 4-byte Integer; Section 2 Asset ID: 4-byteInteger; Machine Name: Text String; Machine Segment ID: 4-byte Integer;Machine Asset ID: 4-byte Integer; Equipment Name: Text String; EquipmentSegment ID: 4-byte Integer; Equipment Asset ID: 4-byte Integer; ShaftName: Text String; Shaft Segment ID: 4-byte Integer; Shaft Asset ID:4-byte Integer; Bearing Name: Text String; Bearing Segment ID: 4-byteInteger; Bearing Asset ID: 4-byte Integer; Probe Name: Text String;Probe Segment ID: 4-byte Integer; Probe Asset ID: 4-byte Integer

Channel Level: Channel #: 4-byte Integer; Direction: 4-byte Integer (incertain examples may be text); Data Type: 4-byte Integer; Reserved Name1: Text String; Reserved Segment ID 1: 4-byte Integer; Reserved Name 2:Text String; Reserved Segment ID 2: 4-byte Integer; Reserved Name 3:Text String; Reserved Segment ID 3: 4-byte Integer

In embodiments, the file with the TDMS format may automatically useproperty or asset information and may make an index file out of thespecific property and asset information to facilitate database searches,may offer a compromise for storing voluminous streams of data because itmay be optimized for storing binary streams of data but may also includesome minimal database structure making many standard SQL operationsfeasible, but the TDMS format and functionality discussed herein may notbe as efficient as a full-fledged SQL relational database. The TDMSformat, however, may take advantage of both worlds in that it maybalance between the class or format of writing and storing large streamsof binary data efficiently and the class or format of a fully relationaldatabase, which facilitates searching, sorting and data retrieval. Inembodiments, an optimum solution may be found in that metadata requiredfor analytical purposes and extracting prescribed lists with panelconditions for stream collection may be stored in the RDS 5930 byestablishing a link between the two database methodologies. By way ofthese examples, relatively large analog data streams may be storedpredominantly as binary storage in the raw data stream archive 5942 forrapid stream loading but with inherent relational SQL type hooks,formats, conventions, or the like. The files with the TDMS format mayalso be configured to incorporate DIAdem™ reporting capability ofLabVIEW™ software in order to provide a further mechanism toconveniently and rapidly facilitate accessing the analog or thestreaming data.

The methods and systems disclosed herein may include, connect to, or beintegrated with a virtual data acquisition instrument and in the manyembodiments, FIG. 37 shows methods and systems that include a virtualstreaming DAQ instrument 6000 also known as a virtual DAQ instrument, aVRDS, or a VSDAQ. In contrast to the DAQ instrument 5002 (FIG. 22), thevirtual DAQ instrument 6000 may be configured so to only include onenative application. In the many examples, the one permitted and onenative application may be a DAQ driver module 6002 that may manage allcommunications with a DAQ Device 6004 which may include streamingcapabilities. In embodiments, other applications, if any, may beconfigured as thin client web applications such as RESTful™ webservices. The one native application, or other applications or services,may be accessible through a DAQ Web API 6010. The DAQ Web API 6010 mayrun in or be accessible through various web browsers.

In embodiments, storage of streaming data, as well as the extraction andprocessing of streaming data into extract and process data, may behandled primarily by a DAQ driver services 6012 under the direction ofthe DAQ Web API 6010. In embodiments, the output from sensors of varioustypes including vibration, temperature, pressure, ultrasound and so onmay be fed into the instrument inputs of the DAQ device 6004. Inembodiments, the signals from the output sensors may be signalconditioned with respect to scaling and filtering and digitized with ananalog to a digital converter. In embodiments, the signals from theoutput sensors may be signals from all relevant channels simultaneouslysampled at a rate sufficient to perform the maximum desired frequencyanalysis. In embodiments, the signals from the output sensors may besampled for a relatively long time, gap-free, as one continuous streamso as to enable a wide array of further post-processing at lowersampling rates with sufficient samples. In further examples, streamingfrequency may be adjusted (and readjusted) to record streaming data atnon-evenly spaced recording. For temperature data, pressure data, andother similar data that may be relatively slow, varying delta timesbetween samples may further improve quality of the data. By way of theabove examples, data may be streamed from a collection of points andthen the next set of data may be collected from additional pointsaccording to a prescribed sequence, route, path, or the like. In themany examples, the portable sensors may be moved to the next locationaccording to the prescribed sequence but not necessarily all of them assome may be used for reference phase or otherwise. In further examples,a multiplexer 6020 may be used to switch to the next collection ofpoints or a mixture of the two methods may be combined.

In embodiments, the sequence and panel conditions that may be used togovern the data collection process using the virtual DAQ instrument 6000may be obtained from a MMP PCSA information store 6022. The MMP PCSAinformation store 6022 may include such items as the hierarchicalstructural relationships of the machine, i.e., a machine contains piecesof equipment in which each piece of equipment contains shafts and eachshaft is associated with bearings, which may be monitored by specifictypes of transducers or probes according to a specific prescribedsequence (routes, path, etc.) with specific panel conditions. By way ofthese examples, the panel conditions may include hardware specificswitch settings or other collection parameters such as sampling rate,AC/DC coupling, voltage range and gain, integration, high and low passfiltering, anti-aliasing filtering, ICP™ transducers and otherintegrated-circuit piezoelectric transducers, 4-20 mA loop sensors, andthe like. The information store 6022 includes other information that maybe stored in what would be machinery specific features that would beimportant for proper analysis including the number of gear teeth for agear, the number of blades in a pump impeller, the number of motor rotorbars, bearing specific parameters necessary for calculating bearingfrequencies, 1× rotating speed (RPMs) of all rotating elements, and thelike.

Upon direction of the DAQ Web API 6010 software, digitized waveforms maybe uploaded using the DAQ driver services 6012 of the virtual DAQinstrument 6000. In embodiments, data may then be fed into an RLN dataand control server 6030 that may store the stream data into a networkstream data repository 6032. Unlike the DAQ instrument 5002, the server6030 may run from within the DAQ driver module 6002. It will beappreciated in light of the disclosure that a separate application mayrequire drivers for running in the native operating system and for thisinstrument only the instrument driver may run natively. In manyexamples, all other applications may be configured to be browser based.As such, a relevant network variable may be very similar to a LabVIEW™shared or network stream variable which may be designed to be accessedover one or more networks or via web applications.

In embodiments, the DAQ web API 6010 may also direct a local datacontrol application 6034 to extract and process the recently obtainedstreaming data and, in turn, convert it to the same or lower samplingrates of sufficient length to provide the desired resolution. This datamay be converted to spectra, then averaged and processed in a variety ofways and stored as EP data, such as on an EP data repository 6040. TheEP data repository 6040 may, in certain embodiments, only be meant fortemporary storage. It will be appreciated in light of the disclosurethat legacy data may require its own sampling rates and resolution andoften this sampling rate may not be integer proportional to the acquiredsampling rate especially for order-sampled data whose sampling frequencyis related directly to an external frequency. The external frequency maytypically be the running speed of the machine or its internalcomponentry, rather than the more-standard sampling rates produced bythe internal crystals, clock functions, and the like of the (e.g.,values of Fmax of 100, 200, 500, 1K, 2K, 5K, 10K, 20K and so on) of theDAQ instrument 5002, 6000. In embodiments, the EP align component of thelocal data control application 6034 is able to fractionally adjust thesampling rate to the non-integer ratio rates that may be more applicableto legacy data sets and therefore drive compatibility with legacysystems. In embodiments, the fractional rates may be converted tointeger ratio rates more readily because the length of the data to beprocessed (or at least that portion of the greater stream of data) isadjustable because of the depth and content of the original acquiredstreaming data by the DAQ instrument 5002, 6000. It will be appreciatedin light of the disclosure that if the data was not streamed and juststored as traditional snap-shots of spectra with the standard values ofFmax, it may very well be impossible to retroactively and accuratelyconvert the acquired data to the order-sampled data. In embodiments, thestream data may be converted, especially for legacy data purposes, tothe proper sampling rate and resolution as described and stored in an EPlegacy data repository 6042. To support legacy data identificationscenarios, a user input 6044 may be included if there is no automatedprocess for identification translation. In embodiments, one suchautomated process for identification translation may include importationof data from a legacy system that may contain a fully standardizedformat such as the Mimosa™ format and sufficient identificationinformation to complete an ID Mapping Table 6048. In further examples,the end user, a legacy data vendor, a legacy data storage facility, orthe like may be able to supply enough info to complete (or sufficientlycomplete) relevant portions of the ID Mapping Table 6048 to provide, inturn, the database schema for the raw data of the legacy system so itmay be readily ingested, saved, and used for analytics in the currentsystems disclosed herein.

FIG. 38 depicts further embodiments and details of the virtual DAQInstrument 6000. In these examples, the DAQ Web API 6010 may control thedata collection process as well as its sequence. The DAQ Web API 6010may provide the capability for editing this process, viewing plots ofthe data, controlling the processing of that data and viewing the outputin all its myriad forms, analyzing the data, including the expertanalysis, communicating with external devices via the DAQ driver module6002, as well as communicating with and transferring both streaming dataand EP data to one or more cloud network facilities 5080 wheneverpossible. In embodiments, the virtual DAQ instrument itself and the DAQWeb API 6010 may run independently of access to the cloud networkfacilities 5080 when local demands may require or simply as a result ofthere being no outside connectivity such use throughout a proprietaryindustrial setting that prevents such signals. In embodiments, the DAQWeb API 6010 may also govern the movement of data, its filtering, aswell as many other housekeeping functions.

The virtual DAQ Instrument 6000 may also include an expert analysismodule 6052. In embodiments, the expert analysis module 6052 may be aweb application or other suitable module that may generate reports 6054that may use machine or measurement point specific information from theMMP PCSA information store 6022 to analyze stream data 6058 using thestream data analyzer module 6050. In embodiments, supervisory control ofthe module 6052 may be provided by the DAQ Web API 6010. In embodiments,the expert analysis may also be supplied (or supplemented) via theexpert system module 5940 that may be resident on one or more cloudnetwork facilities that are accessible via the CDMS 5832. In manyexamples, expert analysis via the cloud may be preferred over localsystems such as the expert analysis module 6052 for various reasons,such as the availability and use of the most up-to-date softwareversion, more processing capability, a bigger volume of historical datato reference and the like. It will be appreciated in light of thedisclosure that it may be important to offer expert analysis when aninternet connection cannot be established so as to provide a redundancy,when needed, for seamless and time efficient operation. In embodiments,this redundancy may be extended to all of the discussed modular softwareapplications and databases where applicable, so each module discussedherein may be configured to provide redundancy to continue operation inthe absence of an internet connection.

FIG. 39 depicts further embodiments and details of many virtual DAQinstruments existing in an online system and connecting through networkendpoints through a central DAQ instrument to one or more cloud networkfacilities. In embodiments, a master DAQ instrument with networkendpoint 6060 is provided along with additional DAQ instruments such asa DAQ instrument with network endpoint 6062, a DAQ instrument withnetwork endpoint 6064, and a DAQ instrument with network endpoint 6068.The master DAQ instrument with network endpoint 6060 may connect withthe other DAQ instruments with network endpoints 6062, 6064, 6068 overLAN 6070. It will be appreciated that each of the instruments 6060,6062, 6064, 6068 may include personal computer, a connected device, orthe like that include Windows™, Linux™, or other suitable operatingsystems to facilitate ease of connection of devices utilizing many wiredand wireless network options such as Ethernet, wireless 802.11g, 900 MHzwireless (e.g., for better penetration of walls, enclosures and otherstructural barriers commonly encountered in an industrial setting), aswell as a myriad of other things permitted by the use of off-the-shelfcommunication hardware when needed.

FIG. 40 depicts further embodiments and details of many functionalcomponents of an endpoint that may be used in the various settings,environments, and network connectivity settings. The endpoint includesendpoint and hardware modules 6080. In embodiments, the endpointhardware modules 6080 may include one or more multiplexers 6082, a DAQinstrument 6084, as well as a computer 6088, computing device, PC, orthe like that may include the multiplexers, DAQ instruments, andcomputers, connected devices and the like, as disclosed herein. Theendpoint software modules 6090 include a data collector application(DCA) 6092 and a raw data server (RDS) 6094. In embodiments, the DCA6092 may be similar to the DAQ API 5052 (FIG. 22) and may be configuredto be responsible for obtaining stream data from the DAQ device 6084 andstoring it locally according to a prescribed sequence or upon userdirectives. In the many examples, the prescribed sequence or userdirectives may be a LabVIEW™ software app that may control and read datafrom the DAQ instruments. For cloud based online systems, the storeddata in many embodiments may be network accessible. In many examples,LabVIEW™ tools may be used to accomplish this with a shared variable ornetwork stream (or subsets of shared variables). Shared variables andthe affiliated network streams may be network objects that may beoptimized for sharing data over the network. In many embodiments, theDCA 6092 may be configured with a graphic user interface that may beconfigured to collect data as efficiently and fast as possible and pushit to the shared variable and its affiliated network stream. Inembodiments, the endpoint raw data server 6094 may be configured to readraw data from the single-process shared variable and may place it with amaster network stream. In embodiments, a raw stream of data fromportable systems may be stored locally and temporarily until the rawstream of data is pushed to the MRDS 5082 (FIG. 22). It will beappreciated in light of the disclosure that on-line system instrumentson a network can be termed endpoints whether local or remote orassociated with a local area network or a wide area network. Forportable data collector applications that may or may not be wirelesslyconnected to one or more cloud network facilities, the endpoint term maybe omitted as described so as to detail an instrument that may notrequire network connectivity.

FIG. 41 depicts further embodiments and details of multiple endpointswith their respective software blocks with at least one of the devicesconfigured as master blocks. Each of the blocks may include a datacollector application (“DCA”) 7000 and a raw data server (“RDS”) 7002.In embodiments, each of the blocks may also include a master raw dataserver module (“MRDS”) 7004, a master data collection and analysismodule (“MDCA”) 7008, and a supervisory and control interface module(“SCI”) 7010. The MRDS 7004 may be configured to read network streamdata (at a minimum) from the other endpoints and may forward it up toone or more cloud network facilities via the CDMS 5832 including thecloud services 5890 and the cloud data 5892. In embodiments, the CDMS5832 may be configured to store the data and to provide web, data, andprocessing services. In these examples, this may be implemented with aLabVIEW™ application that may be configured to read data from thenetwork streams or share variables from all of the local endpoints,write them to the local host PC, local computing device, connecteddevice, or the like, as both a network stream and file with TDMS™formatting. In embodiments, the CDMS 5832 may also be configured to thenpost this data to the appropriate buckets using the LabVIEW or similarsoftware that may be supported by S3™ web service from the Amazon WebServices (“AWS™”) on the Amazon™ web server, or the like and mayeffectively serve as a back-end server. In the many examples, differentcriteria may be enabled or may be set up for when to post data, createor adjust schedules, create or adjust event triggering including a newdata event, create a buffer full message, create or more alarmsmessages, and the like.

In embodiments, the MDCA 7008 may be configured to provide automated aswell as user-directed analyses of the raw data that may include trackingand annotating specific occurrence and in doing so, noting where reportsmay be generated and alarms may be noted. In embodiments, the SCI 7010may be an application configured to provide remote control of the systemfrom the cloud as well as the ability to generate status and alarms. Inembodiments, the SCI 7010 may be configured to connect to, interfacewith, or be integrated into a supervisory control and data acquisition(“SCADA”) control system. In embodiments, the SCI 7010 may be configuredas a LabVIEW™ application that may provide remote control and statusalerts that may be provided to any remote device that may connect to oneor more of the cloud network facilities 5870.

In embodiments, the equipment that is being monitored may include RFIDtags that may provide vital machinery analysis background information.The RFID tags may be associated with the entire machine or associatedwith the individual componentry and may be substituted when certainparts of the machine are replaced, repaired, or rebuilt. The RFID tagsmay provide permanent information relevant to the lifetime of the unitor may also be re-flashed to update with at least a portion of newinformation. In many embodiments, the DAQ instruments 5002 disclosedherein may interrogate the one or more RFID chips to learn of themachine, its componentry, its service history, and the hierarchicalstructure of how everything is connected including drive diagrams, wirediagrams, and hydraulic layouts. In embodiments, some of the informationthat may be retrieved from the RFID tags includes manufacturer,machinery type, model, serial number, model number, manufacturing date,installation date, lots numbers, and the like. By way of these examples,machinery type may include the use of a Mimosa™ format table includinginformation about one or more of the following motors, gearboxes, fans,and compressors. The machinery type may also include the number ofbearings, their type, their positioning, and their identificationnumbers. The information relevant to one or more fans includes fan type,number of blades, number of vanes, and number of belts. It will beappreciated in light of the disclosure that other machines and theircomponentry may be similarly arranged hierarchically with relevantinformation all of which may be available through interrogation of oneor more RFID chips associated with the one or more machines.

In embodiments, data collection in an industrial environment may includerouting analog signals from a plurality of sources, such as analogsensors, to a plurality of analog signal processing circuits. Routing ofanalog signals may be accomplished by an analog crosspoint switch thatmay route any of a plurality of analog input signals to any of aplurality of outputs, such as to analog and/or digital outputs. Routingof inputs to outputs in an analog signal crosspoint switch in anindustrial environment may be configurable, such as by an electronicsignal to which a switch portion of the analog crosspoint switch isresponsive.

In embodiments, the analog crosspoint switch may receive analog signalsfrom a plurality of analog signal sources in the industrial environment.Analog signal sources may include sensors that produce an analog signal.Sensors that produce an analog signal that may be switched by the analogcrosspoint switch may include sensors that detect a condition andconvert it to an analog signal that may be representative of thecondition, such as converting a condition to a corresponding voltage.Exemplary conditions that may be represented by a variable voltage mayinclude temperature, friction, sound, light, torque,revolutions-per-minute, mechanical resistance, pressure, flow rate, andthe like, including any of the conditions represented by inputs sourcesand sensors disclosed throughout this disclosure and the documentsincorporated herein by reference. Other forms of analog signal mayinclude electrical signals, such as variable voltage, variable current,variable resistance, and the like.

In embodiments, the analog crosspoint switch may preserve one or moreaspects of an analog signal being input to it in an industrialenvironment. Analog circuits integrated into the switch may providebuffered outputs. The analog circuits of the analog crosspoint switchmay follow an input signal, such as an input voltage to produce abuffered representation on an output. This may alternatively beaccomplished by relays (mechanical, solid state, and the like) thatallow an analog voltage or current present on an input to propagate to aselected output of the analog switch.

In embodiments, an analog crosspoint switch in an industrial environmentmay be configured to switch any of a plurality of analog inputs to anyof a plurality of analog outputs. An example embodiment includes a MIMO,multiplexed configuration. An analog crosspoint switch may bedynamically configurable so that changes to the configuration causes achange in the mapping of inputs to outputs. A configuration change mayapply to one or more mappings so that a change in mapping may result inone or more of the outputs being mapped to different input than beforethe configuration change.

In embodiments, the analog crosspoint switch may have more inputs thanoutputs, so that only a subset of inputs can be routed to outputsconcurrently. In other embodiments, the analog crosspoint switch mayhave more outputs than inputs, so that either a single input may be madeavailable currently on multiple outputs, or at least one output may notbe mapped to any input.

In embodiments, an analog crosspoint switch in an industrial environmentmay be configured to switch any of a plurality of analog inputs to anyof a plurality of digital outputs. To accomplish conversion from analoginputs to digital outputs, an analog-to-digital converter circuit may beconfigured on each input, each output, or at intermediate points betweenthe input(s) and output(s) of the analog crosspoint switch. Benefits ofincluding digitization of analog signals in an analog crosspoint switchthat may be located close to analog signal sources may include reducingsignal transport costs and complexity that digital signal communicationhas over analog, reducing energy consumption, facilitating detection andregulation of aberrant conditions before they propagate throughout anindustrial environment, and the like. Capturing analog signals close totheir source may also facilitate improved signal routing management thatis more tolerant of real world effects such as requiring that multiplesignals be routed simultaneously. In this example, a portion of thesignals can be captured (and stored) locally while another portion canbe transferred through the data collection network. Once the datacollection network has available bandwidth, the locally stored signalscan be delivered, such as with a time stamp indicating the time at whichthe data was collected. This technique may be useful for applicationsthat have concurrent demand for data collection channels that exceed thenumber of channels available. Sampling control may also be based on anindication of data worth sampling. As an example, a signal source, suchas a sensor in an industrial environment may provide a data valid signalthat transmits an indication of when data from the sensor is available.

In embodiments, mapping inputs of the analog crosspoint switch tooutputs may be based on a signal route plan for a portion of theindustrial environment that may be presented to the crosspoint switch.The signal route plan may be used in a method of data collection in theindustrial environment that may include routing a plurality of analogsignals along a plurality of analog signal paths. The method may includeconnecting the plurality of analog signals individually to inputs of theanalog crosspoint switch that may be configured with a route plan. Thecrosspoint switch may, responsively to the configured route plan, routea portion of the plurality of analog signals to a portion of theplurality of analog signal paths.

In embodiments, the analog crosspoint switch may include at least onehigh current output drive circuit that may be suitable for routing theanalog signal along a path that requires high current. In embodiments,the analog crosspoint switch may include at least one voltage-limitedinput that may facilitate protecting the analog crosspoint switch fromdamage due to excessive analog input signal voltage. In embodiments, theanalog crosspoint switch may include at least one current limited inputthat may facilitate protecting the analog crosspoint switch from damagedue to excessive analog input current. The analog crosspoint switch maycomprise a plurality of interconnected relays that may facilitaterouting the input(s) to the output(s) with little or no substantivesignal loss.

In embodiments, an analog crosspoint switch may include processingfunctionality, such as signal processing and the like (e.g., aprogrammed processor, special purpose processor, a digital signalprocessor, and the like) that may detect one or more analog input signalconditions. In response to such detection, one or more actions may beperformed, such as setting an alarm, sending an alarm signal to anotherdevice in the industrial environment, changing the crosspoint switchconfiguration, disabling one or more outputs, powering on or off aportion of the switch, changing a state of an output, such as a generalpurpose digital or analog output, and the like. In embodiments, theswitch may be configured to process inputs for producing a signal on oneor more of the outputs. The inputs to use, processing algorithm for theinputs, condition for producing the signal, output to use, and the likemay be configured in a data collection template.

In embodiments, an analog crosspoint switch may comprise greater than 32inputs and greater than 32 outputs. A plurality of analog crosspointswitches may be configured so that even though each switch offers fewerthan 32 inputs and 32 outputs it may be configured to facilitateswitching any of 32 inputs to any of 32 outputs spread across theplurality of crosspoint switches.

In embodiments, an analog crosspoint switch suitable for use in anindustrial environment may comprise four or fewer inputs and four orfewer outputs. Each output may be configurable to produce an analogoutput that corresponds to the mapped analog input or it may beconfigured to produce a digital representation of the correspondingmapped input.

In embodiments, an analog crosspoint switch for use in an industrialenvironment may be configured with circuits that facilitate replicatingat least a portion of attributes of the input signal, such as current,voltage range, offset, frequency, duty cycle, ramp rate, and the likewhile buffering (e.g., isolating) the input signal from the outputsignal. Alternatively, an analog crosspoint switch may be configuredwith unbuffered inputs/outputs, thereby effectively producing abi-directional based crosspoint switch).

In embodiments, an analog crosspoint switch for use in an industrialenvironment may include protected inputs that may be protected fromdamaging conditions, such as through use of signal conditioningcircuits. Protected inputs may prevent damage to the switch and todownstream devices to which the switch outputs connect. As an example,inputs to such an analog crosspoint switch may include voltage clippingcircuits that prevent a voltage of an input signal from exceeding aninput protection threshold. An active voltage adjustment circuit mayscale an input signal by reducing it uniformly so that a maximum voltagepresent on the input does not exceed a safe threshold value. As anotherexample, inputs to such an analog crosspoint switch may include currentshunting circuits that cause current beyond a maximum input protectioncurrent threshold to be diverted through protection circuits rather thanenter the switch. Analog switch inputs may be protected fromelectrostatic discharge and/or lightning strikes. Other signalconditioning functions that may be applied to inputs to an analogcrosspoint switch may include voltage scaling circuitry that attempts tofacilitate distinguishing between valid input signals and low voltagenoise that may be present on the input. However, in embodiments, inputsto the analog crosspoint switch may be unbuffered and/or unprotected tomake the least impact on the signal. Signals such as alarm signals, orsignals that cannot readily tolerate protection schemes, such as thoseschemes described above herein may be connected to unbuffered inputs ofthe analog crosspoint switch.

In embodiments, an analog crosspoint switch may be configured withcircuitry, logic, and/or processing elements that may facilitate inputsignal alarm monitoring. Such an analog crosspoint switch may detectinputs meeting alarm conditions and in response thereto, switch inputs,switch mapping of inputs to outputs, disable inputs, disable outputs,issue an alarm signal, activate/deactivate a general-purpose output, orthe like.

In embodiments, a system for collecting data in an industrialenvironment may include an analog crosspoint switch that may be adaptedto selectively power up or down portions of the analog crosspoint switchor circuitry associated with the analog crosspoint switch, such as inputprotection devices, input conditioning devices, switch control devicesand the like. Portions of the analog crosspoint switch that may bepowered on/off may include outputs, inputs, sections of the switch andthe like. In an example, an analog crosspoint switch may include amodular structure that may separate portions of the switch intoindependently powered sections. Based on conditions, such as an inputsignal meeting a criterion or a configuration value being presented tothe analog crosspoint switch, one or more modular sections may bepowered on/off.

In embodiments, a system for collecting data in an industrialenvironment may include an analog crosspoint switch that may be adaptedto perform signal processing including, without limitation, providing avoltage reference for detecting an input crossing the voltage reference(e.g., zero volts for detecting zero-crossing signals), a phase-lockloop to facilitate capturing slow frequency signals (e.g., low-speedrevolution-per-minute signals and detecting their corresponding phase),deriving input signal phase relative to other inputs, deriving inputsignal phase relative to a reference (e.g., a reference clock), derivinginput signal phase relative to detected alarm input conditions and thelike. Other signal processing functions of such an analog crosspointswitch may include oversampling of inputs for delta-sigma A/D, toproduce lower sampling rate outputs, to minimize AA filter requirementsand the like. Such an analog crosspoint switch may support long blocksampling at a constant sampling rate even as inputs are switched, whichmay facilitate input signal rate independence and reduce complexity ofsampling scheme(s). A constant sampling rate may be selected from aplurality of rates that may be produced by a circuit, such as a clockdivider circuit that may make available a plurality of components of areference clock.

In embodiments, a system for collecting data in an industrialenvironment may include an analog crosspoint switch that may be adaptedto support implementing data collection/data routing templates in theindustrial environment. The analog crosspoint switch may implement adata collection/data routing template based on conditions in theindustrial environment that it may detect or derive, such as an inputsignal meeting one or more criteria (e.g., transition of a signal from afirst condition to a second, lack of transition of an input signalwithin a predefined time interface (e.g., inactive input) and the like).

In embodiments, a system for collecting data in an industrialenvironment may include an analog crosspoint switch that may be adaptedto be configured from a portion of a data collection template.Configuration may be done automatically (without needing humanintervention to perform a configuration action or change inconfiguration), such as based on a time parameter in the template andthe like. Configuration may be done remotely, e.g., by sending a signalfrom a remote location that is detectable by a switch configurationfeature of the analog crosspoint switch. Configuration may be donedynamically, such as based on a condition that is detectable by aconfiguration feature of the analog crosspoint switch (e.g., a timer, aninput condition, an output condition, and the like). In embodiments,information for configuring an analog crosspoint switch may be providedin a stream, as a set of control lines, as a data file, as an indexeddata set, and the like. In embodiments, configuration information in adata collection template for the switch may include a list of each inputand a corresponding output, a list of each output function (active,inactive, analog, digital and the like), a condition for updating theconfiguration (e.g., an input signal meeting a condition, a triggersignal, a time (relative to another time/event/state, or absolute), aduration of the configuration, and the like. In embodiments,configuration of the switch may be input signal protocol aware so thatswitching from a first input to a second input for a given output mayoccur based on the protocol. In an example, a configuration change maybe initiated with the switch to switch from a first video signal to asecond video signal. The configuration circuitry may detect the protocolof the input signal and switch to the second video signal during asynchronization phase of the video signal, such as during horizontal orvertical refresh. In other examples, switching may occur when one ormore of the inputs are at zero volts. This may occur for a sinusoidalsignal that transitions from below zero volts to above zero volts.

In embodiments, a system for collecting data in an industrialenvironment may include an analog crosspoint switch that may be adaptedto provide digital outputs by converting analog signals input to theswitch into digital outputs. Converting may occur after switching theanalog inputs based on a data collection template and the like. Inembodiments, a portion of the switch outputs may be digital and aportion may be analog. Each output, or groups thereof, may beconfigurable as analog or digital, such as based on analog crosspointswitch output configuration information included in or derived from adata collection template. Circuitry in the analog crosspoint switch maysense an input signal voltage range and intelligently configure ananalog-to-digital conversion function accordingly. As an example, afirst input may have a voltage range of 12 volts and a second input mayhave a voltage range of 24 volts. Analog-to-digital converter circuitsfor these inputs may be adjusted so that the full range of the digitalvalue (e.g., 256 levels for an 8-bit signal) will map substantiallylinearly to 12 volts for the first input and 24 volts for the secondinput.

In embodiments, an analog crosspoint switch may automatically configureinput circuitry based on characteristics of a connected analog signal.Examples of circuitry configuration may include setting a maximumvoltage, a threshold based on a sensed maximum threshold, a voltagerange above and/or below a ground reference, an offset reference, andthe like. The analog crosspoint switch may also adapt inputs to supportvoltage signals, current signals, and the like. The analog crosspointswitch may detect a protocol of an input signal, such as a video signalprotocol, audio signal protocol, digital signal protocol, protocol basedon input signal frequency characteristics, and the like. Other aspectsof inputs of the analog crosspoint switch that may be adapted based onthe incoming signal may include a duration of sampling of the signal,and comparator or differential type signals, and the like.

In embodiments, an analog crosspoint switch may be configured withfunctionality to counteract input signal drift and/or leakage that mayoccur when an analog signal is passed through it over a long period oftime without changing value (e.g., a constant voltage). Techniques mayinclude voltage boost, current injection, periodic zero referencing(e.g., temporarily connecting the input to a reference signal, such asground, applying a high resistance pathway to the ground reference, andthe like).

In embodiments, a system for data collection in an industrialenvironment may include an analog crosspoint switch deployed in anassembly line comprising conveyers and/or lifters. A power rollerconveyor system includes many rollers that deliver product along a path.There may be many points along the path that may be monitored for properoperation of the rollers, load being placed on the rollers, accumulationof products, and the like. A power roller conveyor system may alsofacilitate moving product through longer distances and therefore mayhave a large number of products in transport at once. A system for datacollection in such an assembly environment may include sensors thatdetect a wide range of conditions as well as at a large number ofpositions along the transport path. As a product progresses down thepath, some sensors may be active and others, such as those that theproduct has passed maybe inactive. A data collection system may use ananalog crosspoint switch to select only those sensors that are currentlyor anticipated to be active by switching from inputs that connect toinactive sensors to those that connect to active sensors and therebyprovide the most useful sensor signals to data detection and/orcollection and/or processing facilities. In embodiments, the analogcrosspoint switch may be configured by a conveyor control system thatmonitors product activity and instructs the analog crosspoint switch todirect different inputs to specific outputs based on a control programor data collection template associated with the assembly environment.

In embodiments, a system for data collection in an industrialenvironment may include an analog crosspoint switch deployed in afactory comprising use of fans as industrial components. In embodiments,fans in a factory setting may provide a range of functions includingdrying, exhaust management, clean air flow and the like. In aninstallation of a large number of fans, monitoring fan rotational speed,torque, and the like may be beneficial to detect an early indication ofa potential problem with air flow being produced by the fans. However,concurrently monitoring each of these elements for a large number offans may be inefficient. Therefore, sensors, such as tachometers, torquemeters, and the like may be disposed at each fan and their analog outputsignal(s) may be provided to an analog crosspoint switch. With a limitednumber of outputs, or at least a limited number of systems that canprocess the sensor data, the analog crosspoint switch may be used toselect among the many sensors and pass along a subset of the availablesensor signals to data collection, monitoring, and processing systems.In an example, sensor signals from sensors disposed at a group of fansmay be selected to be switched onto crosspoint switch outputs. Uponsatisfactory collection and/or processing of the sensor signals for thisgroup of fans, the analog crosspoint switch may be reconfigured toswitch signals from another group of fans to be processed.

In embodiments, a system for data collection in an industrialenvironment may include an analog crosspoint switch deployed as anindustrial component in a turbine-based power system. Monitoring forvibration in turbine systems, such as hydro-power systems, has beendemonstrated to provide advantages in reduction in down time. However,with a large number of areas to monitor for vibration, particularly foron-line vibration monitoring, including relative shaft vibration,bearings absolute vibration, turbine cover vibration, thrust bearingaxial vibration, stator core vibrations, stator bar vibrations, statorend winding vibrations, and the like, it may be beneficial to selectamong this list over time, such as taking samples from sensors for eachof these types of vibration a few at a time. A data collection systemthat includes an analog crosspoint switch may provide this capability byconnecting each vibration sensor to separate inputs of the analogcrosspoint switch and configuring the switch to output a subset of itsinputs. A vibration data processing system, such as a computer, maydetermine which sensors to pass through the analog crosspoint switch andconfigure an algorithm to perform the vibration analysis accordingly. Asan example, sensors for capturing turbine cover vibration may beselected in the analog crosspoint switch to be passed on to a systemthat is configured with an algorithm to determine turbine covervibration from the sensor signals. Upon completion of determiningturbine cover vibration, the crosspoint switch may be configured to passalong thrust bearing axial vibration sensor signals and a correspondingvibration analysis algorithm may be applied to the data. In this way,each type of vibration may be analyzed by a single processing systemthat works cooperatively with an analog crosspoint switch to passspecific sensor signals for processing.

Referring to FIG. 44, an analog crosspoint switch for collecting data inan industrial environment is depicted. An analog crosspoint switch 7022may have a plurality of inputs 7024 that connect to sensors 7026 in theindustrial environment. The analog crosspoint switch 7022 may alsocomprise a plurality of outputs 7028 that connect to data collectioninfrastructure, such as analog-to-digital converters 7030, analogcomparators 7032, and the like. The analog crosspoint switch 7022 mayfacilitate connecting one or more inputs 7024 to one or more outputs7028 by interpreting a switch control value that may be provided to itby a controller 7034 and the like.

An example system for data collection in an industrial environmentcomprising includes analog signal sources that each connect to at leastone input of an analog crosspoint switch including a plurality of inputsand a plurality of outputs; where the analog crosspoint switch isconfigurable to switch a portion of the input signal sources to aplurality of the outputs.

In certain embodiments, the analog crosspoint switch further includes ananalog-to-digital converter that converts a portion of analog signalsinput to the crosspoint switch into representative digital signals; aportion of the outputs including analog outputs and a portion of theoutputs comprises digital outputs; and/or where the analog crosspointswitch is adapted to detect one or more analog input signal conditions.Any one or more of the example embodiments include the analog inputsignal conditions including a voltage range of the signal, and where theanalog crosspoint switch responsively adjusts input circuitry to complywith detected voltage range.

An example system of data collection in an industrial environmentincludes a number of industrial sensors that produce analog signalsrepresentative of a condition of an industrial machine in theenvironment being sensed by the number of industrial sensors, acrosspoint switch that receives the analog signals and routes the analogsignals to separate analog outputs of the crosspoint switch based on asignal route plan presented to the crosspoint switch. In certainembodiments, the analog crosspoint switch further includes ananalog-to-digital converter that converts a portion of analog signalsinput to the crosspoint switch into representative digital signals;where a portion of the outputs include analog outputs and a portion ofthe outputs include digital outputs; where the analog crosspoint switchis adapted to detect one or more analog input signal conditions; wherethe one or more analog input signal conditions include a voltage rangeof the signal, and/or where the analog crosspoint switch responsivelyadjusts input circuitry to comply with detected voltage range.

An example method of data collection in an industrial environmentincludes routing a number of analog signals along a plurality of analogsignal paths by connecting the plurality of analog signals individuallyto inputs of an analog crosspoint switch, configuring the analogcrosspoint switch with data routing information from a data collectiontemplate for the industrial environment routing, and routing with theconfigured analog crosspoint switch a portion of the number of analogsignals to a portion the plurality of analog signal paths. In certainfurther embodiments, at least one output of the analog crosspoint switchincludes a high current driver circuit; at least one input of the analogcrosspoint switch includes a voltage limiting circuit; and/or at leastone input of the analog crosspoint switch includes a current limitingcircuit. In certain further embodiments, the analog crosspoint switchincludes a number of interconnected relays that facilitate connectingany of a number of inputs to any of a plurality of outputs; the analogcrosspoint switch further including an analog-to-digital converter thatconverts a portion of analog signals input to the crosspoint switch intoa representative digital signal; the analog crosspoint switch furtherincluding signal processing functionality to detect one or more analoginput signal conditions, and in response thereto, to perform an action(e.g., set an alarm, change switch configuration, disable one or moreoutputs, power off a portion of the switch, change a state of a generalpurpose (digital/analog) output, etc.); where a portion of the outputsare analog outputs and a portion of the outputs are digital outputs;where the analog crosspoint switch is adapted to detect one or moreanalog input signal conditions; where the analog crosspoint switch isadapted to take one or more actions in response to detecting the one ormore analog input signal conditions, the one more actions includingsetting an alarm, sending an alarm signal, changing a configuration ofthe analog crosspoint switch, disabling an output, powering off aportion of the analog crosspoint switch, powering on a portion of theanalog crosspoint switch, and/or controlling a general purpose output ofthe analog crosspoint switch.

An example system includes a power roller of a conveyor, including anyof the described operations of an analog crosspoint switch. Withoutlimitation, further example embodiments include sensing conditions ofthe power roller by the sensors to determine a rate of rotation of thepower roller, a load being transported by the power roller, power beingconsumed by the power roller, and/or a rate of acceleration of the powerroller. An example system includes a fan in a factory setting, includingany of the described operations of an analog crosspoint switch. Withoutlimitation, certain further embodiments include sensors disposed tosense conditions of the fan, including a fan blade tip speed, torque,back pressure, RPMs, and/or a volume of air per unit time displaced bythe fan. An example system includes a turbine in a power generationenvironment, including any of the described operations of an analogcrosspoint switch. Without limitation, certain further embodimentsinclude a number of sensors disposed to sense conditions of the turbine,where the sensed conditions include a relative shaft vibration, anabsolute vibration of bearings, a turbine cover vibration, a thrustbearing axial vibration, vibrations of stators or stator cores,vibrations of stator bars, and/or vibrations of stator end windings.

In embodiments, methods and systems of data collection in an industrialenvironment may include a plurality of industrial condition sensing andacquisition modules that may include at least one programmable logiccomponent per module that may control a portion of the sensing andacquisition functionality of its module. The programmable logiccomponents on each of the modules may be interconnected by a dedicatedlogic bus that may include data and control channels. The dedicatedlogic bus may extend logically and/or physically to other programmablelogic components on other sensing and acquisition modules. Inembodiments, the programmable logic components may be programmed via thededicated interconnection bus, via a dedicated programming portion ofthe dedicated interconnection bus, via a program that is passed betweenprogrammable logic components, sensing and acquisition modules, or wholesystems. A programmable logic component for use in an industrialenvironment data sensing and acquisition system may be a ComplexProgrammable Logic Device, an Application-Specific Integrated Circuit,microcontrollers, and combinations thereof.

A programmable logic component in an industrial data collectionenvironment may perform control functions associated with datacollection. Control examples include power control of analog channels,sensors, analog receivers, analog switches, portions of logic modules(e.g., a logic board, system, and the like) on which the programmablelogic component is disposed, self-power-up/down, self-sleep/wake up, andthe like. Control functions, such as these and others, may be performedin coordination with control and operational functions of otherprogrammable logic components, such as other components on a single datacollection module and components on other such modules. Other functionsthat a programmable logic component may provide may include generationof a voltage reference, such as a precise voltage reference for inputsignal condition detection. A programmable logic component may generate,set, reset, adjust, calibrate, or otherwise determine the voltage of thereference, its tolerance, and the like. Other functions of aprogrammable logic component may include enabling a digital phase lockloop to facilitate tracking slowly transitioning input signals, andfurther to facilitate detecting the phase of such signals. Relativephase detection may also be implemented, including phase relative totrigger signals, other analog inputs, on-board references (e.g.,on-board timers), and the like. A programmable logic component may beprogrammed to perform input signal peak voltage detection and controlinput signal circuitry, such as to implement auto-scaling of the inputto an operating voltage range of the input. Other functions that may beprogrammed into a programmable logic component may include determiningan appropriate sampling frequency for sampling inputs independently oftheir operating frequencies. A programmable logic component may beprogrammed to detect a maximum frequency among a plurality of inputsignals and set a sampling frequency for each of the input signals thatis greater than the detected maximum frequency.

A programmable logic component may be programmed to configure andcontrol data routing components, such as multiplexers, crosspointswitches, analog-to-digital converters, and the like, to implement adata collection template for the industrial environment. A datacollection template may be included in a program for a programmablelogic component. Alternatively, an algorithm that interprets a datacollection template to configure and control data routing resources inthe industrial environment may be included in the program.

In embodiments, one or more programmable logic components in anindustrial environment may be programmed to perform smart-band signalanalysis and testing. Results of such analysis and testing may includetriggering smart band data collection actions, that may includereconfiguring one or more data routing resources in the industrialenvironment. A programmable logic component may be configured to performa portion of smart band analysis, such as collection and validation ofsignal activity from one or more sensors that may be local to theprogrammable logic component. Smart band signal analysis results from aplurality of programmable logic components may be further processed byother programmable logic components, servers, machine learning systems,and the like to determine compliance with a smart band.

In embodiments, one or more programmable logic components in anindustrial environment may be programmed to control data routingresources and sensors for outcomes, such as reducing power consumption(e.g., powering on/off resources as needed), implementing security inthe industrial environment by managing user authentication, and thelike. In embodiments, certain data routing resources, such asmultiplexers and the like, may be configured to support certain inputsignal types. A programmable logic component may configure the resourcesbased on the type of signals to be routed to the resources. Inembodiments, the programmable logic component may facilitatecoordination of sensor and data routing resource signal type matching byindicating to a configurable sensor a protocol or signal type to presentto the routing resource. A programmable logic component may facilitatedetecting a protocol of a signal being input to a data routing resource,such as an analog crosspoint switch and the like. Based on the detectedprotocol, the programmable logic component may configure routingresources to facilitate support and efficient processing of theprotocol. In an example, a programmable logic component configured datacollection module in an industrial environment may implement anintelligent sensor interface specification, such as IEEE 1451.2intelligent sensor interface specification.

In embodiments, distributing programmable logic components across aplurality of data sensing, collection, and/or routing modules in anindustrial environment may facilitate greater functionality and localinter-operational control. In an example, modules may performoperational functions independently based on a program installed in oneor more programmable logic components associated with each module. Twomodules may be constructed with substantially identical physicalcomponents but may perform different functions in the industrialenvironment based on the program(s) loaded into programmable logiccomponent(s) on the modules. In this way, even if one module were toexperience a fault, or be powered down, other modules may continue toperform their functions due at least in part to each having its ownprogrammable logic component(s). In embodiments, configuring a pluralityof programmable logic components distributed across a plurality of datacollection modules in an industrial environment may facilitatescalability in terms of conditions in the environment that may besensed, the number of data routing options for routing sensed datathroughout the industrial environment, the types of conditions that maybe sensed, the computing capability in the environment, and the like.

In embodiments, a programmable logic controller-configured datacollection and routing system may facilitate validation of externalsystems for use as storage nodes, such as for a distributed ledger, andthe like. A programmable logic component may be programmed to performvalidation of a protocol for communicating with such an external system,such as an external storage node.

In embodiments, programming of programmable logic components, such asCPLDs and the like may be performed to accommodate a range of datasensing, collection and configuration differences. In embodiments,reprogramming may be performed on one or more components when addingand/or removing sensors, when changing sensor types, when changingsensor configurations or settings, when changing data storageconfigurations, when embedding data collection template(s) into deviceprograms, when adding and/or removing data collection modules (e.g.,scaling a system), when a lower cost device is used that may limitfunctionality or resources over a higher cost device, and the like. Aprogrammable logic component may be programmed to propagate programs forother programmable components via a dedicated programmable logic deviceprogramming channel, via a daisy chain programming architecture, via amesh of programmable logic components, via a hub-and-spoke architectureof interconnected components, via a ring configuration (e.g., using acommunication token, and the like).

In embodiments, a system for data collection in an industrialenvironment comprising distributed programmable logic devices connectedby a dedicated control bus may be deployed with drilling machines in anoil and gas harvesting environment, such as an oil and/or gas field. Adrilling machine has many active portions that may be operated,monitored, and adjusted during a drilling operation. Sensors to monitora crown block may be physically isolated from sensors for monitoring ablowout preventer and the like. To effectively maintain control of thiswide range and diverse disposition of sensors, programmable logiccomponents, such as Complex Programmable Logic Devices (“CPLD”) may bedistributed throughout the drilling machine. While each CPLD may beconfigured with a program to facilitate operation of a limited set ofsensors, at least portions of the CPLD may be connected by a dedicatedbus for facilitating coordination of sensor control, operation and use.In an example, a set of sensors may be disposed proximal to a mud pumpor the like to monitor flow, density, mud tank levels, and the like. Oneor more CPLD may be deployed with each sensor (or a group of sensors) tooperate the sensors and sensor signal routing and collection resources.The CPLD in this mud pump group may be interconnected by a dedicatedcontrol bus to facilitate coordination of sensor and data collectionresource control and the like. This dedicated bus may extend physicallyand/or logically beyond the mud pump control portion of the drillmachine so that CPLD of other portions (e.g., the crown block and thelike) may coordinate data collection and related activity throughportions of the drilling machine.

In embodiments, a system for data collection in an industrialenvironment comprising distributed programmable logic devices connectedby a dedicated control bus may be deployed with compressors in an oiland gas harvesting environment, such as an oil and/or gas field.Compressors are used in the oil and gas industry for compressing avariety of gases and purposes include flash gas, gas lift, reinjection,boosting, vapor-recovery, casing head and the like. Collecting data fromsensors for these different compressor functions may requiresubstantively different control regimes. Distributing CPLDs programmedwith different control regimes is an approach that may accommodate thesediverse data collection requirements. One or more CPLDs may be disposedwith sets of sensors for the different compressor functions. A dedicatedcontrol bus may be used to facilitate coordination of control and/orprogramming of CPLDs in and across compressor instances. In an example,a CPLD may be configured to manage a data collection infrastructure forsensors disposed to collect compressor-related conditions for flash gascompression; a second CPLD or group of CPLDs may be configured to managea data collection infrastructure for sensors disposed to collectcompressor related conditions for vapor-recovery gas compression. Thesegroups of CPLDs may operate control programs.

In embodiments, a system for data collection in an industrialenvironment comprising distributed programmable logic devices connectedby a dedicated control bus may be deployed in a refinery with turbinesfor oil and gas production, such as with modular impulse steam turbines.A system for collection of data from impulse steam turbines may beconfigured with a plurality of condition sensing and collection modulesadapted for specific functions of an impulse steam turbine. DistributingCPLDs along with these modules can facilitate adaptable data collectionto suit individual installations. As an example, blade conditions, suchas tip rotational rate, temperature rise of the blades, impulsepressure, blade acceleration rate, and the like may be captured in datacollection modules configured with sensors for sensing these conditions.Other modules may be configured to collect data associated with valves(e.g., in a multi-valve configuration, one or more modules may beconfigured for each valve or for a set of valves), turbine exhaust(e.g., radial exhaust data collection may be configured differently thanaxial exhaust data collection), turbine speed sensing may be configureddifferently for fixed versus variable speed implementations, and thelike. Additionally, impulse gas turbine systems may be installed withother systems, such as combined cycle systems, cogeneration systems,solar power generation systems, wind power generation systems,hydropower generation systems, and the like. Data collectionrequirements for these installations may also vary. Using a CPLD-based,modular data collection system that uses a dedicated interconnection busfor the CPLDs may facilitate programming and/or reprogramming of eachmodule directly in place without having to shut down or physicallyaccess each module.

Referring to FIG. 45, an exemplary embodiment of a system for datacollection in an industrial environment comprising distributed CPLDsinterconnected by a bus for control and/or programming thereof isdepicted. An exemplary data collection module 7200 may comprise one ormore CPLDs 7206 for controlling one or more data collection systemresources, such as sensors 7202 and the like. Other data collectionresources that a CPLD may control may include crosspoint switches,multiplexers, data converters, and the like. CPLDs on a module may beinterconnected by a bus, such as a dedicated logic bus 7204 that mayextend beyond a data collection module to CPLDs on other data collectionmodules. Data collection modules, such as the module 7200 may beconfigured in the environment, such as on an industrial machine 7208(e.g., an impulse gas turbine) and/or 7210 (e.g., a co-generationsystem), and the like. Control and/or configuration of the CPLDs may behandled by a controller 7212 in the environment. Data collection androuting resources and interconnection (not shown) may also be configuredwithin and among data collection modules 7200 as well as between andamong the industrial machines 7208 and 7210, and/or with externalsystems, such as Internet portals, data analysis servers, and the liketo facilitate data collection, routing, storage, analysis, and the like.

An example system for data collection in an industrial environmentincludes a number of industrial condition sensing and acquisitionmodules, with a programmable logic component disposed on each of themodules, where the programmable logic component controls a portion ofthe sensing and acquisition functional of the corresponding module. Thesystem includes communication bus that is dedicated to interconnectingthe at least one programmable logic component disposed on at least oneof the plurality of modules. In embodiments, the communication busextends to other programmable logic components on other sensing andacquisition modules.

In certain further embodiments, a system includes the programmable logiccomponent programmed via the communication bus, the communication busincluding a portion dedicated to programming of the programmable logiccomponents, controlling a portion of the sensing and acquisitionfunctionality of a module by a power control function such as:controlling power of a sensor, a multiplexer, a portion of the module,and/or controlling a sleep mode of the programmable logic component;controlling a portion of the sensing and acquisition functionality of amodule by providing a voltage reference to a sensor and/or ananalog-to-digital converter disposed on the module, by detecting arelative the phase of at least two analog signals derived from at leasttwo sensors disposed on the module; by controlling sampling of dataprovided by at least one sensor disposed on the module; by detecting apeak voltage of a signal provided by a sensor disposed on the module;and/or by configuring at least one multiplexer disposed on the module byspecifying to the multiplexer a mapping of at least one input and oneoutput. In certain embodiments, the communication bus extends to otherprogrammable logic components on other condition sensing and/oracquisition modules. In certain embodiments, a module may be anindustrial environment condition sensing module. In certain embodiments,a module control program includes an algorithm for implementing anintelligent sensor interface communication protocol, such as an IEEE1451.2 compatible intelligent sensor interface communication protocol.In certain embodiments, a programmable logic component includesconfiguring the programmable logic component and/or the sensing oracquisition module to implement a smart band data collection template.Example and non-limiting programmable logic components include fieldprogrammable gate arrays, complex programmable logic devices, and/ormicrocontrollers.

An example system includes a drilling machine for oil and gas field use,with a condition sensing and/or acquisition module to monitor aspects ofa drilling machine. Without limitation, a further example systemincludes monitoring a compressor and/or monitoring an impulse steamengine.

In embodiments, a system for data collection in an industrialenvironment may include a trigger signal and at least one data signalthat share a common output of a signal multiplexer and upon detection ofa condition in the industrial environment, such as a state of thetrigger signal, the common output is switched to propagate either thedata signal or the trigger signal. Sharing an output between a datasignal and a trigger signal may also facilitate reducing a number ofindividually routed signals in an industrial environment. Benefits ofreducing individually routed signals may include reducing the number ofinterconnections between data collection module, thereby reducing thecomplexity of the industrial environment. Trade-offs for reducingindividually routed signals may include increasing sophistication oflogic at signal switching modules to implement the detection andconditional switching of signals. A net benefit of this added localizedlogic complexity may be an overall reduction in the implementationcomplexity of such a data collection system in an industrialenvironment.

Exemplary deployment environments may include environments with triggersignal channel limitations, such as existing data collection systemsthat do not have separate trigger support for transporting an additionaltrigger signal to a module with sufficient computing sophistication toperform trigger detection. Another exemplary deployment may includesystems that require at least some autonomous control for performingdata collection.

In embodiments, a system for data collection in an industrialenvironment may include an analog switch that switches between a firstinput, such as a trigger input and a second input, such as a data inputbased on a condition of the first input. A trigger input may bemonitored by a portion of the analog switch to detect a change in thesignal, such as from a lower voltage to a higher voltage relative to areference or trigger threshold voltage. In embodiments, a device thatmay receive the switched signal from the analog switch may monitor thetrigger signal for a condition that indicates a condition for switchingfrom the trigger input to the data input. When a condition of thetrigger input is detected, the analog switch may be reconfigured, todirect the data input to the same output that was propagating thetrigger output.

In embodiments, a system for data collection in an industrialenvironment may include an analog switch that directs a first input toan output of the analog switch until such time as the output of theanalog switch indicates that a second input should be directed to theoutput of the analog switch. The output of the analog switch maypropagate a trigger signal to the output. In response to the triggersignal propagating through the switch transitioning from a firstcondition (e.g., a first voltage below a trigger threshold voltagevalue) to a second condition (e.g., a second voltage above the triggerthreshold voltage value), the switch may stop propagating the triggersignal and instead propagate another input signal to the output. Inembodiments, the trigger signal and the other data signal may berelated, such as the trigger signal may indicate a presence of an objectbeing placed on a conveyer and the data signal represents a strainplaced on the conveyer.

In embodiments, to facilitate timely detection of the trigger condition,a rate of sampling of the output of the analog switch may be adjustable,so that, for example, the rate of sampling is higher while the triggersignal is propagated and lower when the data signal is propagated.Alternatively, a rate of sampling may be fixed for either the trigger orthe data signal. In embodiments, the rate of sampling may be based on apredefined time from trigger occurrence to trigger detection and may befaster than a minimum sample rate to capture the data signal.

In embodiments, routing a plurality of hierarchically organized triggersonto another analog channel may facilitate implementing a hierarchicaldata collection triggering structure in an industrial environment. Adata collection template to implement a hierarchical trigger signalarchitecture may include signal switch configuration and function datathat may facilitate a signal switch facility, such as an analogcrosspoint switch or multiplexer to output a first input trigger in ahierarchy, and based on the first trigger condition being detected,output a second input trigger in the hierarchy on the same output as thefirst input trigger by changing an internal mapping of inputs tooutputs. Upon detection of the second input trigger condition, theoutput may be switched to a data signal, such as data from a sensor inan industrial environment.

In embodiments, upon detection of a trigger condition, in addition toswitching from the trigger signal to a data signal, an alarm may begenerated and optionally propagated to a higher functioningdevice/module. In addition to switching to a data signal, upon detectionof a state of the trigger, sensors that otherwise may be disabled orpowered down may be energized/activated to begin to produce data for thenewly selected data signal. Activating might alternatively includesending a reset or refresh signal to the sensor(s).

In embodiments, a system for data collection in an industrialenvironment may include a system for routing a trigger signal onto adata signal path in association with a gearbox of an industrial vehicle.Combining a trigger signal onto a signal path that is also used for adata signal may be useful in gearbox applications by reducing the numberof signal lines that need to be routed, while enabling advancedfunctions, such as data collection based on pressure changes in thehydraulic fluid and the like. As an example, a sensor may be configuredto detect a pressure difference in the hydraulic fluid that exceeds acertain threshold as may occur when the hydraulic fluid flow is directedback into the impeller to give higher torque at low speeds. The outputof such a sensor may be configured as a trigger for collecting dataabout the gearbox when operating at low speeds. In an example, a datacollection system for an industrial environment may have a multiplexeror switch that facilitates routing either a trigger or a data channelover a single signal path. Detecting the trigger signal from thepressure sensor may result in a different signal being routed throughthe same line that the trigger signal was routed by switching a set ofcontrols. A multiplexer may, for example, output the trigger signaluntil the trigger signal is detected as indicating that the outputshould be changed to the data signal. As a result of detecting thehigh-pressure condition, a data collection activity may be activated sothat data can be collected using the same line that was recently used bythe trigger signal.

In embodiments, a system for data collection in an industrialenvironment may include a system for routing a trigger signal onto adata signal path in association with a vehicle suspension for truck andcar operation. Vehicle suspension, particularly active suspension mayinclude sensors for detecting road events, suspension conditions, andvehicle data, such as speed, steering, and the like. These conditionsmay not always need to be detected, except, for example, upon detectionof a trigger condition. Therefore, combining the trigger conditionsignal and at least one data signal on a single physical signal routingpath could be implemented. Doing so may reduce costs due to fewerphysical connections required in such a data collection system. In anexample, a sensor may be configured to detect a condition, such as a pothole, to which the suspension must react. Data from the suspension maybe routed along the same signal routing path as this road conditiontrigger signal so that upon detection of the pot hole, data may becollected that may facilitate determining aspects of the suspension'sreaction to the pot hole.

In embodiments, a system for data collection in an industrialenvironment may include a system for routing a trigger signal onto adata signal path in association with a turbine for power generation in apower station. A turbine used for power generation may be retrofittedwith a data collection system that optimizes existing data signal linesto implement greater data collection functions. One such approachinvolves routing new sources of data over existing lines. Whilemultiplexing signals generally satisfies this need, combining a triggersignal with a data signal via a multiplexer or the like can furtherimprove data collection. In an example, a first sensor may include athermal threshold sensor that may measure the temperature of an aspectof a power generation turbine. Upon detection of that trigger (e.g., bythe temperature rising above the thermal threshold), a data collectionsystem controller may send a different data collection signal over thesame line that was used to detect the trigger condition. This may beaccomplished by a controller or the like sensing the trigger signalchange condition and then signaling to the multiplexer to switch fromthe trigger signal to a data signal to be output on the same line as thetrigger signal for data collection. In this example, when a turbine isdetected as having a portion that exceeds its safe thermal threshold, asecondary safety signal may be routed over the trigger signal path andmonitored for additional safety conditions, such as overheating and thelike.

Referring to FIG. 46, an embodiment of routing a trigger signal over adata signal path in a data collection system in an industrialenvironment is depicted. A signal multiplexer 7400 may receive a triggersignal on a first input from a sensor or other trigger source 7404 and adata signal on a second input from a sensor for detecting a temperatureassociated with an industrial machine in the environment 7402. Themultiplexer 7400 may be configured to output the trigger signal onto anoutput signal path 7406. A data collection module 7410 may process thesignal on the data path 7406 looking for a change in the signalindicative of a trigger condition provided from the trigger sensor 7404through the multiplexer 7400. Upon detection, a control output 7408 maybe changed and thereby control the multiplexer 7400 to start outputtingdata from the temperature probe 7402 by switching an internal switch orthe like that may control one or more of the inputs that may be routedto the output 7406. The data collection facility 7410 may activate adata collection template in response to the detected trigger that mayinclude switching the multiplexer and collecting data into triggereddata storage 7412. Upon completion of the data collection activity, themultiplexer control signal 7408 may revert to its initial condition sothat the trigger sensor 7404 may be monitored again.

An example system for data collection in an industrial environmentincludes an analog switch that directs a first input to an output of theanalog switch until such time as the output of the analog switchindicates that a second input should be directed to the output of theanalog switch. In certain further embodiments, the example systemincludes: where the output of the analog switch indicated that thesecond input should be directed to the output based on the outputtransitioning from a pending condition to a triggered condition. Inembodiments, the triggered condition includes detecting the outputpresenting a voltage above a trigger voltage value; routing a number ofsignals with the analog switch from inputs on the analog switch tooutputs on the analog switch in response to the output of the analogswitch indicating that the second input should be directed to theoutput; sampling the output of the analog switch at a rate that exceedsa rate of transition for a number of signals input to the analog switch;and/or generating an alarm signal when the output of the analog switchindicates that a second input should be directed to the output of theanalog switch.

An example system for data collection in an industrial environmentincludes an analog switch that switches between a first input and asecond input based on a condition of the first input. In certain furtherembodiments, the condition of the first input comprises the first inputpresenting a triggered condition, and/or the triggered conditionincludes detecting the first input presenting a voltage above a triggervoltage value. In certain embodiments, the analog switch includesrouting a plurality of signals with the analog from inputs on the analogswitch to outputs on the analog switch based on the condition of thefirst input, sampling an input of the analog switch at a rate thatexceeds a rate of transition for a plurality of signals input to theanalog switch, and/or generating an alarm signal based on the conditionof the first input.

An example system for data collection in an industrial environmentincludes a trigger signal and at least one data signal that share acommon output of a signal multiplexer, and upon detection of apredefined state of the trigger signal, the common output is configuredto propagate the at least one data signal through the signalmultiplexer. In certain further embodiments, the signal multiplexer isan analog multiplexer, the predefined state of the trigger signal isdetected on the common output, detection of the predefined state of thetrigger signal includes detecting the common output presenting a voltageabove a trigger voltage value, the multiplexer includes routing aplurality of signals with the multiplexer from inputs on the multiplexerto outputs on the multiplexer in response to detection of the predefinedstate of the trigger signal, the multiplexer includes sampling theoutput of the multiplexer at a rate that exceeds a rate of transitionfor a plurality of signals input to the multiplexer, the multiplexerincludes generating an alarm in response to detection of the predefinedstate of the trigger signal, and/or the multiplexer includes activatingat least one sensor to produce the at least one data signal. Withoutlimitation, example systems include: monitoring a gearbox of anindustrial vehicle by directing a trigger signal representing acondition of the gearbox to an output of the analog switch until suchtime as the output of the analog switch indicates that a second inputrepresenting a condition of the gearbox related to the trigger signalshould be directed to the output of the analog switch; monitoring asuspension system of an industrial vehicle by directing a trigger signalrepresenting a condition of the suspension to an output of the analogswitch until such time as the output of the analog switch indicates thata second input representing a condition of the suspension related to thetrigger signal should be directed to the output of the analog switch;and/or monitoring a power generation turbine by directing a triggersignal representing a condition of the power generation turbine to anoutput of the analog switch until such time as the output of the analogswitch indicates that a second input representing a condition of thepower generation turbine related to the trigger signal should bedirected to the output of the analog switch.

In embodiments, a system for data collection in an industrialenvironment may include a data collection system that monitors at leastone signal for a set of collection band parameters and upon detection ofa parameter from the set of collection band parameters in the signal,configures collection of data from a set of sensors based on thedetected parameter. The set of selected sensors, the signal, and the setof collection band parameters may be part of a smart bands datacollection template that may be used by the system when collecting datain an industrial environment. A motivation for preparing a smart-bandsdata collection template may include monitoring a set of conditions ofan industrial machine to facilitate improved operation, reduce downtime, preventive maintenance, failure prevention, and the like. Based onanalysis of data about the industrial machine, such as those conditionsthat may be detected by the set of sensors, an action may be taken, suchas notifying a user of a change in the condition, adjusting operatingparameters, scheduling preventive maintenance, triggering datacollection from additional sets of sensors, and the like. An example ofdata that may indicate a need for some action may include changes thatmay be detectable through trends present in the data from the set ofsensors. Another example is trends of analysis values derived from theset of sensors.

In embodiments, the set of collection band parameters may include valuesreceived from a sensor that is configured to sense a condition of theindustrial machine (e.g., bearing vibration). However, a set ofcollection band parameters may instead be a trend of data received fromthe sensor (e.g., a trend of bearing vibration across a plurality ofvibration measurements by a bearing vibration sensor). In embodiments, aset of collection band parameters may be a composite of data and/ortrends of data from a plurality of sensors (e.g., a trend of data fromon-axis and off-axis vibration sensors). In embodiments, when a datavalue derived from one or more sensors as described herein issufficiently close to a value of data in the set of collection bandparameters, the data collection activity from the set of sensors may betriggered. Alternatively, a data collection activity from the set ofsensors may be triggered when a data value derived from the one or moresensors (e.g., trends and the like) falls outside of a set of collectionband parameters. In an example, a set of data collection band parametersfor a motor may be a range of rotational speeds from 95% to 105% of aselect operational rotational speed. So long as a trend of rotationalspeed of the motor stays within this range, a data collection activitymay be deferred. However, when the trend reaches or exceeds this range,then a data collection activity, such as one defined by a smart bandsdata collection template may be triggered.

In embodiments, triggering a data collection activity, such as onedefined by a smart bands data collection template, may result in achange to a data collection system for an industrial environment thatmay impact aspects of the system such as data sensing, switching,routing, storage allocation, storage configuration, and the like. Thischange to the data collection system may occur in near real time to thedetection of the condition; however, it may be scheduled to occur in thefuture. It may also be coordinated with other data collection activitiesso that active data collection activities, such as a data collectionactivity for a different smart bands data collection template, cancomplete prior to the system being reconfigured to meet the smart bandsdata collection template that is triggered by the sensed conditionmeeting the smart bands data collection trigger.

In embodiments, processing of data from sensors may be cumulative overtime, over a set of sensors, across machines in an industrialenvironment, and the like. While a sensed value of a condition may besufficient to trigger a smart bands data collection template activity,data may need to be collected and processed over time from a pluralityof sensors to generate a data value that may be compared to a set ofdata collection band parameters for conditionally triggering the datacollection activity. Using data from multiple sensors and/or processingdata, such as to generate a trend of data values and the like mayfacilitate preventing inconsequential instances of a sensed data valuebeing outside of an acceptable range from causing unwarranted smartbands data collection activity. In an example, if a vibration from abearing is detected outside of an acceptable range infrequently, thentrending for this value over time may be useful to detect if thefrequency is increasing, decreasing, or staying substantially constantor within a range of values. If the frequency of such a value is foundto be increasing, then such a trend is indicative of changes occurringin operation of the industrial machine as experienced by the bearing. Anacceptable range of values of this trended vibration value may beestablished as a set of data collection band parameters against whichvibration data for the bearing will be monitored. When the trendedvibration value is outside of this range of acceptable values, a smartbands data collection activity may be activated.

In embodiments, a system for data collection in an industrialenvironment that supports smart band data collection templates may beconfigured with data processing capability at a point of sensing of oneor more conditions that may trigger a smart bands data collectiontemplate data collection activity, such as: by use of an intelligentsensor that may include data processing capabilities; by use of aprogrammable logic component that interfaces with a sensor and processesdata from the sensor; by use of a computer processor, such as amicroprocessor and the like disposed proximal to the sensor; and thelike. In embodiments, processing of data collected from one or moresensors for detecting a smart bands template data collection activitymay be performed by remote processors, servers, and the like that mayhave access to data from a plurality of sensors, sensor modules,industrial machines, industrial environments, and the like.

In embodiments, a system for data collection in an industrialenvironment may include a data collection system that monitors anindustrial environment for a set of parameters, and upon detection of atleast one parameter, configures the collection of data from a set ofsensors and causes a data storage controller to adapt a configuration ofdata storage facilities to support collection of data from the set ofsensors based on the detected parameter. The methods and systemsdescribed herein for conditionally changing a configuration of a datacollection system in an industrial environment to implement a smartbands data collection template may further include changes to datastorage architectures. As an example, a data storage facility may bedisposed on a data collection module that may include one or moresensors for monitoring conditions in an industrial environment. Thislocal data storage facility may typically be configured for rapidmovement of sensed data from the module to a next level sensing orprocessing module or server. When a smart bands data collectioncondition is detected, sensor data from a plurality of sensors may needto be captured concurrently. To accommodate this concurrent collection,the local memory may be reconfigured to capture data from each of theplurality of sensors in a coordinated manner, such as repeatedlysampling each of the sensors synchronously, or with a known offset, andthe like, to build up a set of sensed data that may be much larger thanwould typically be captured and moved through the local memory. Astorage control facility for controlling the local storage may monitorthe movement of sensor data into and out of the local data storage,thereby ensuring safe movement of data from the plurality of sensors tothe local data storage and on to a destination, such as a server,networked storage facility, and the like. The local data storagefacility may be configured so that data from the set of sensorsassociated with a smart bands data collection template are securelystored and readily accessible as a set of smart band data to facilitateprocessing the smart band-specific data. As an example, local storagemay comprise non-volatile memory (NVM). To prepare for data collectionin response to a smart band data collection template being triggered,portions of the NVM may be erased to prepare the NVM to receive data asindicated in the template.

In embodiments, multiple sensors may be arranged into a set of sensorsfor condition-specific monitoring. Each set, which may be a logical setof sensors, may be selected to provide information about elements in anindustrial environment that may provide insight into potential problems,root causes of problems, and the like. Each set may be associated with acondition that may be monitored for compliance with an acceptable rangeof values. The set of sensors may be based on a machine architecture,hierarchy of components, or a hierarchy of data that contributes to afinding about a machine that may usefully be applied to maintaining orimproving performance in the industrial environment. Smart band sensorsets may be configured based on expert system analysis of complexconditions, such as machine failures and the like. Smart band sensorsets may be arranged to facilitate knowledge gathering independent of aparticular failure mode or history. Smart band sensor sets may bearranged to test a suggested smart band data collection template priorto implementing it as part of an industrial machine operations program.Gathering and processing data from sets of sensors may facilitatedetermining which sensors contribute meaningful data to the set, andthose sensors that do not contribute can be removed from the set. Smartband sensor sets may be adjusted based on external data, such asindustry studies that indicate the types of sensor data that is mosthelpful to reduce failures in an industrial environment.

In embodiments, a system for data collection in an industrialenvironment may include a data collection system that monitors at leastone signal for compliance to a set of collection band conditions andupon detection of a lack of compliance, configures the collection ofdata from a predetermined set of sensors associated with the monitoredsignal. Upon detection of a lack of compliance, a collection bandtemplate associated with the monitored signal may be accessed, andresources identified in the template may be configured to perform thedata collection. In embodiments, the template may identify sensors toactivate, data from the sensors to collect, duration of collection orquantity of data to be collected, destination (e.g., memory structure)to store the collected data, and the like. In embodiments, a smart bandmethod for data collection in an industrial environment may includeperiodic collection of data from one or more sensors configured to sensea condition of an industrial machine in the environment. The collecteddata may be checked against a set of criteria that define an acceptablerange of the condition. Upon validation that the collected data iseither approaching one end of the acceptable limit or is beyond theacceptable range of the condition, data collection may commence from asmart-band group of sensors associated with the sensed condition basedon a smart-band collection protocol configured as a data collectiontemplate. In embodiments, an acceptable range of the condition is basedon a history of applied analytics of the condition. In embodiments, uponvalidation of the acceptable range being exceeded, data storageresources of a module in which the sensed condition is detected may beconfigured to facilitate capturing data from the smart band group ofsensors.

In embodiments, monitoring a condition to trigger a smart band datacollection template data collection action may be: in response to: aregulation, such as a safety regulation; in response to an upcomingactivity, such as a portion of the industrial environment being shutdown for preventive maintenance; in response to sensor data missing fromroutine data collection activities; and the like. In embodiments, inresponse to a faulty sensor or sensor data missing from a smart bandtemplate data collection activity, one or more alternate sensors may betemporarily included in the set of sensors so as to provide data thatmay effectively substitute for the missing data in data processingalgorithms.

In embodiments, smart band data collection templates may be configuredfor detecting and gathering data for smart band analysis coveringvibration spectra, such as vibration envelope and current signature forspectral regions or peaks that may be combinations of absolute frequencyor factors of machine related parameters, vibration time waveforms fortime-domain derived calculations including, without limitation: RMSoverall, peak overall, true peak, crest factor, and the like; vibrationvectors, spectral energy humps in various regions (e.g., low-frequencyregion, high frequency region, low orders, and the like);pressure-volume analysis and the like.

In embodiments, a system for data collection that applies smart banddata collection templates may be applied to an industrial environment,such as ball screw actuators in an automated production environment.Smart band analysis may be applied to ball screw actuators in industrialenvironments such as precision manufacturing or positioning applications(e.g., semiconductor photolithography machines, and the like). As atypical primary objective of using a ball screw is for precisepositioning, detection of variation in the positioning mechanism canhelp avoid costly defective production runs. Smart bands triggering anddata collection may help in such applications by detecting, throughsmart band analysis, potential variations in the positioning mechanismsuch as in the ball screw mechanism, a worm drive, a linear motor, andthe like. In an example, data related to a ball screw positioning systemmay be collected with a system for data collection in an industrialenvironment as described herein. A plurality of sensors may beconfigured to collect data such as screw torque, screw direction, screwspeed, screw step, screw home detection, and the like. Some portion ofthis data may be processed by a smart bands data analysis facility todetermine if variances, such as trends in screw speed as a function oftorque, approach or exceed an acceptable threshold. Upon such adetermination, a data collection template for the ball screw productionsystem may be activated to configure the data sensing, routing, andcollection resources of the data collection system to perform datacollection to facilitate further analysis. The smart band datacollection template facilitates rapid collection of data from othersensors than screw speed and torque, such as position, direction,acceleration, and the like by routing data from corresponding sensorsover one or more signal paths to a data collector. The duration andorder of collection of the data from these sources may be specified inthe smart bands data collection template so that data required forfurther analysis is effectively captured.

In embodiments, a system for data collection that applies smart banddata collection templates to configure and utilize data collection androuting infrastructure may be applied to ventilation systems in miningenvironments. Ventilation provides a crucial role in mining safety.Early detection of potential problems with ventilation equipment can beaided by applying a smart bands approach to data collection in such anenvironment. Sensors may be disposed for collecting information aboutventilation operation, quality, and performance throughout a miningoperation. At each ventilation device, ventilation-related elements,such as fans, motors, belts, filters, temperature gauges, voltage,current, air quality, poison detection, and the like may be configuredwith a corresponding sensor. While variation in any one element (e.g.,air volume per minute, and the like) may not be indicative of a problem,smart band analysis may be applied to detect trends over time that maybe suggestive of potential problems with ventilation equipment. Toperform smart bands analysis, data from a plurality of sensors may berequired to form a basis for analysis. By implementing data collectionsystems for ventilation stations, data from a ventilation system may becaptured. In an example, a smart band analysis may be indicated for aventilation station. In response to this indication, a data collectionsystem may be configured to collect data by routing data from sensorsdisposed at the ventilation station to a central monitoring facilitythat may gather and analyze data from several ventilation stations.

In embodiments, a system for data collection that applies smart banddata collection templates to configure and utilize data collection androuting infrastructure may be applied to drivetrain data collection andanalysis in mining environments. A drivetrain, such as a drivetrain fora mining vehicle, may include a range of elements that could benefitfrom use of the methods and systems of data collection in an industrialenvironment as described herein. In particular, smart band-based datacollection may be used to collect data from heavy duty mining vehicledrivetrains under certain conditions that may be detectable by smartbands analysis. A smart bands-based data collection template may be usedby a drivetrain data collection and routing system to configure sensors,data paths, and data collection resources to perform data collectionunder certain circumstances, such as those that may indicate anunacceptable trend of drivetrain performance. A data collection systemfor an industrial drivetrain may include sensing aspects of anon-steering axle, a planetary steering axle, driveshafts, (e.g., mainand wing shafts), transmissions, (e.g., standard, torque converters,long drop), and the like. A range of data related to these operationalparts may be collected. However, data for support and structural membersthat support the drivetrain may also need to be collected for thoroughsmart band analysis. Therefore, collection across this wide range ofdrivetrain-related components may be triggered based on a smart bandanalysis determination of a need for this data. In an example, a smartband analysis may indicate potential slippage between a main and wingdriveshaft that may represented by an increasing trend in response delaytime of the wing drive shaft to main drive shaft operation. In responseto this increasing trend, data collection modules disposed throughoutthe mining vehicle's drive train may be configured to route data fromlocal sensors to be collected and analyzed by data collectors. Miningvehicle drivetrain smart based data collection may include a range oftemplates based on which type of trend is detected. If a trend relatedto a steering axle is detected, a data collection template to beimplemented may be different in sensor content, duration, and the likethan for a trend related to power demand for a normalized payload. Eachtemplate could configure data sensing, routing, and collection resourcesthroughout the vehicle drive train accordingly.

Referring to FIG. 47, a system for data collection in an industrialenvironment that facilitates data collection for smart band analysis isdepicted. A system for data collection in an industrial environment mayinclude a smart band analysis data collection template repository 7600in which smart band templates 7610 for data collection systemconfiguration and collection of data may be stored and accessed by adata collection controller 7602. The templates 7610 may include datacollection system configuration 7604 and operation information 7606 thatmay identify sensors, collectors, signal paths, and information forinitiation and coordination of collection, and the like. The controller7602 may receive an indication, such as a command from a smart bandanalysis facility 7608 to select and implement a specific smart bandtemplate 7610. The controller 7602 may access the template 7610 andconfigure the data collection system resources based on the informationin that template. In embodiments, the template may identify: specificsensors; a multiplexer/switch configuration, data collectiontrigger/initiation signals and/or conditions, time duration and/oramount of data for collection; destination of collected data;intermediate processing, if any; and any other useful information,(e.g., instance identifier, and the like). The controller 7602 mayconfigure and operate the data collection system to perform thecollection for the smart band template and optionally return the systemconfiguration to a previous configuration.

An example system for data collection in an industrial environmentincludes a data collection system that monitors at least one signal fora set of collection band parameters and, upon detection of a parameterfrom the set of collection band parameters, configures portions of thesystem and performs collection of data from a set of sensors based onthe detected parameter. In certain further embodiments, the signalincludes an output of a sensor that senses a condition in the industrialenvironment, where the set of collection band parameters comprisesvalues derivable from the signal that are beyond an acceptable range ofvalues derivable from the signal; where the at least one signal includesan output of a sensor that senses a condition in the industrialenvironment. In embodiments, configuring portions of the system includesconfiguring a storage facility to accept data collected from the set ofsensors; where configuring portions of the system includes configuring adata routing portion includes at least one of: an analog crosspointswitch, a hierarchical multiplexer, an analog-to-digital converter, anintelligent sensor, and/or a programmable logic component. Inembodiments, detection of a parameter from the set of collection bandparameters comprises detecting a trend value for the signal being beyondan acceptable range of trend values; and/or where configuring portionsof the system includes implementing a smart band data collectiontemplate associated with the detected parameter. In certain embodiments,a data collection system monitors a signal for data values within a setof acceptable data values that represent acceptable collection bandconditions for the signal and, upon detection of a data value for the atleast one signal outside of the set of acceptable data values, triggersa data collection activity that causes collecting data from apredetermined set of sensors associated with the monitored signal. Incertain further embodiment, a data collection system includes the signalincluding an output of a sensor that senses a condition in theindustrial environment; where the set of acceptable data value includesvalues derivable from the signal that are within an acceptable range ofvalues derivable from the signal; configuring a storage facility of thesystem to facilitate collecting data from the predetermined set ofsensors in response to the detection of a data value outside of the setof acceptable data values; configuring a data routing portion of thesystem including an analog crosspoint switch, a hierarchicalmultiplexer, an analog-to-digital converter, an intelligent sensor,and/or a programmable logic component in response to detecting a datavalue outside of the set of acceptable data values; where detection of adata value for the signal outside of the set of acceptable data valuesincludes detecting a trend value for the signal being beyond anacceptable range of trend values; and/or where the data collectionactivity is defined by a smart band data collection template associatedwith the detected parameter.

An example method for data collection in an industrial environmentcomprising includes an operation to collect data from sensor(s)configured to sense a condition of an industrial machine in theenvironment; an operation to check the collected data against a set ofcriteria that define an acceptable range of the condition; and inresponse to the collected data violating the acceptable range of thecondition, an operation to collect data from a smart-band group ofsensors associated with the sensed condition based on a smart-bandcollection protocol configured as a smart band data collection template.In certain further embodiments, a method includes where violating theacceptable range of the condition includes a trend of the data from thesensor(s) approaching a maximum value of the acceptable range; where thesmart-band group of sensors is defined by the smart band data collectiontemplate; where the smart band data collection template includes a listof sensors to activate, data from the sensors to collect, duration ofcollection of data from the sensors, and/or a destination location forstoring the collected data; where collecting data from a smart-bandgroup of sensors includes configuring at least one data routing resourceof the industrial environment that facilitates routing data from thesmart band group of sensors to a plurality of data collectors; and/orwhere the set of criteria includes a range of trend values derived byprocessing the data from sensor(s).

Without limitation, an example system monitors a ball screw actuator inan automated production environment, and monitors at least one signalfrom the ball screw actuator for a set of collection band parametersand, upon detection of a parameter from the set of collection bandparameters, configures portions of the system and performs collection ofdata from a set of sensors disposed to monitor conditions of the ballscrew actuator based on the detected parameter; another example systemmonitors a ventilation system in a mining environment, and monitors atleast one signal from the ventilation system for a set of collectionband parameters and, upon detection of a parameter from the set ofcollection band parameters, configures portions of the system andperforms collection of data from a set of sensors disposed to monitorconditions of the ventilation system based on the detected parameter; anexample system monitors a drivetrain of a mining vehicle, and monitorsat least one signal from the drive train for a set of collection bandparameters and, upon detection of a parameter from the set of collectionband parameters, configures portions of the system and performscollection of data from a set of sensors disposed to monitor conditionsof the drivetrain based on the detected parameter.

In embodiments, a system for data collection in an industrialenvironment may automatically configure local and remote data collectionresources and may perform data collection from a plurality of systemsensors that are identified as part of a group of sensors that producedata that is required to perform operational deflection shape rendering.In embodiments, the system sensors are distributed throughout structuralportions of an industrial machine in the industrial environment. Inembodiments, the system sensors sense a range of system conditionsincluding vibration, rotation, balance, friction, and the like. Inembodiments, automatically configuring is in response to a condition inthe environment being detected outside of an acceptable range ofcondition values. In embodiments, a sensor in the identified group ofsystem sensors senses the condition.

In embodiments, a system for data collection in an industrialenvironment may configure a data collection plan, such as a template, tocollect data from a plurality of system sensors distributed throughout amachine to facilitate automatically producing an operational deflectionshape visualization (“ODSV”) based on machine structural information anda data set used to produce an ODSV of the machine.

In embodiments, a system for data collection in an industrialenvironment may configure a data collection template for collecting datain an industrial environment by identifying sensors disposed for sensingconditions of preselected structural members of an industrial machine inthe environment based on an ODSV of the industrial machine. Inembodiments, the template may include an order and timing of datacollection from the identified sensors.

In embodiments, methods and systems for data collection in an industrialenvironment may include a method of establishing an acceptable range ofsensor values for a plurality of industrial machine condition sensors byvalidating an operational deflection shape visualization of structuralelements of the machine as exhibiting deflection within an acceptablerange. In embodiments, data from the plurality of sensors used in thevalidated ODSV define the acceptable range of sensor values.

In embodiments, a system for data collection in an industrialenvironment may include a plurality of data sources, such as sensors,that may be grouped for coordinated data collection to provide datarequired to produce an ODSV. Information regarding the sensors to group,data collection coordination requirements, and the like may be retrievedfrom an ODSV data collection template. Coordinated data collection mayinclude concurrent data collection. To facilitate concurrent datacollection from a portion of the group of sensors, sensor routingresources of the system for data collection may be configured, such asby configuring a data multiplexer to route data from the portion of thegroup of sensors to which it connects to data collectors. Inembodiments, each such source that connects an input of the multiplexermay be routed within the multiplexer to separate outputs so that datafrom all of the connected sources may be routed on to data collectionelements of the industrial environment. In embodiments, the multiplexermay include data storage capabilities that may facilitate sharing acommon output for at least a portion of the inputs. In embodiments, amultiplexer may include data storage capabilities and data bus-enabledoutputs so that data for each source may be captured in a memory andtransmitted over a data bus, such as a data bus that is common to theoutputs of the multiplexer. In embodiments, sensors may be smart sensorsthat may include data storage capabilities and may send data from thedata storage to the multiplexer in a coordinated manner that supportsuse of a common output of the multiplexer and/or use of a common databus.

In embodiments, a system for data collection in an industrialenvironment may comprise templates for configuring the data collectionsystem to collect data from a plurality of sensors to perform ODSV for aplurality of deflection shapes. Individual templates may be configuredfor visualization of looseness, soft joints, bending, twisting, and thelike. Individual deflection shape data collection templates may beconfigured for different portions of a machine in an industrialenvironment.

In embodiments, a system for data collection in an industrialenvironment may facilitate operational deflection shape visualizationthat may include visualization of locations of sensors that contributeddata to the visualization. In the visualization, each sensor thatcontributed data to generate the visualization may be indicated by avisual element. The visual element may facilitate user access toinformation about the sensor, such as location, type, representativedata contributed, path of data from the sensor to a data collector, adeflection shape template identifier, a configuration of a switch ormultiplexer through which the data is routed, and the like. The visualelement may be determined by associating sensor identificationinformation received from a sensor with information, such as a sensormap, that correlates sensor identification information with physicallocation in the environment. The information may appear in thevisualization in response to the visual element representing the sensorbeing selected, such as by a user positioning a cursor on the sensorvisual element.

In embodiments, ODSV may benefit from data satisfying a phaserelationship requirement. A data collection system in the environmentmay be configured to facilitate collecting data that complies with thephase relationship requirement. Alternatively, the data collectionsystem may be configured to collect data from a plurality of sensorsthat contains data that satisfies the phase relationship requirementsbut may also include data that does not. A post processing operationthat may access phase detection data may select a subset of thecollected data.

In embodiments, a system for data collection in an industrialenvironment may include a multiplexer receiving data from a plurality ofsensors and multiplexing the received data for delivery to a datacollector. The data collector may process the data to facilitate ODSV.ODSV may require data from several different sensors, and may benefitfrom using a reference signal, such as data from a sensor, whenprocessing data from the different sensors. The multiplexer may beconfigured to provide data from the different sensors, such as byswitching among its inputs over time so that data from each sensor maybe received by the data collector. However, the multiplexer may includea plurality of outputs so that at least a portion of the inputs may berouted to least two of the plurality of outputs. Therefore, inembodiments, a multiple output multiplexer may be configured tofacilitate data collection that may be suitable for ODSV by routing areference signal from one of its inputs (e.g., data from anaccelerometer) to one of its outputs and multiplexing data from aplurality of its outputs onto one or more of its outputs whilemaintaining the reference signal output routing. A data collector maycollect the data from the reference output and use that to align themultiplexed data from the other sensors.

In embodiments, a system for data collection in an industrialenvironment may facilitate ODSV through coordinated data collectionrelated to conveyors for mining applications. Mining operations may relyon conveyor systems to move material, supplies, and equipment into andout of a mine. Mining operations may typically operate around the clock;therefore, conveyor downtime may have a substantive impact onproductivity and costs. Advanced analysis of conveyor and relatedsystems that focuses on secondary affects that may be challenging todetect merely through point observation may be more readily detected viaODSV. Capturing operational data related to vibration, stresses, and thelike can facilitate ODSV. However, coordination of data capture providesmore reliable results. Therefore, a data collection system that may havesensors dispersed throughout a conveyor system can be configured tofacilitate such coordinated data collection. In an example, capture ofdata affecting structural components of a conveyor, such as; landingpoints and the horizontal members that connect them and support theconveyer between landing points; conveyer segment handoff points; motormounts; mounts of conveyer rollers and the like may need to becoordinated with data related to conveyor dynamic loading, drivesystems, motors, gates, and the like. A system for data collection in anindustrial environment, such as a mining environment, may include datasensing and collection modules placed throughout the conveyor atlocations such as segment handoff points, drive systems, and the like.Each module may be configured by one or more controllers, such asprogrammable logic controllers, that may be connected through a physicalor logical (e.g., wireless) communication bus that aids in performingcoordinated data collection. To facilitate coordination, a referencesignal, such as a trigger and the like, may be communicated among themodules for use when collecting data. In embodiments, data collectionand storage may be performed at each module so as to reduce the need forreal-time transfer of sensed data throughout the mining environment.Transfer of data from the modules to an ODSV processing facility may beperformed after collection, or as communication bandwidth between themodules and the processing facility allows. ODSV can provide insightinto conditions in the conveyer, such as deflection of structuralmembers that may, over time cause premature failure. Coordinated datacollection with a data collection system for use in an industrialenvironment, such as mining, can enable ODSV that may reduce operatingcosts by reducing downtime due to unexpected component failure.

In embodiments, a system for data collection in an industrialenvironment may facilitate operational deflection shape visualizationthrough coordinated data collection related to fans for miningapplications. Fans provide a crucial function in mining operations ofmoving air throughout a mine to provide ventilation, equipment cooling,combustion exhaust evacuation, and the like. Ensuring reliable and oftencontinuous operation of fans may be critical for miner safety andcost-effective operations. Dozens or hundreds of fans may be used inlarge mining operations. Fans, such as fans for ventilation management,may include circuit, booster, and auxiliary types. High capacityauxiliary fans may operate at high speeds, over 2500 RPMs. PerformingODSV may reveal important reliability information about fans deployed ina mining environment. Collecting the range of data needed for ODSV ofmining fans may be performed by a system for collecting data inindustrial environments as described herein. In embodiments, sensingelements, such as intelligent sensing and data collection modules may bedeployed with fans and/or fan subsystems. These modules may exchangecollection control information (e.g., over a dedicated control bus andthe like) so that data collection may be coordinated in time and phaseto facilitate ODSV.

A large auxiliary fan for use in mining may be constructed fortransportability into and through the mine and therefore may include afan body, intake and outlet ports, dilution valves, protection cage,electrical enclosure, wheels, access panels, and other structural and/oroperational elements. The ODSV of such an auxiliary fan may requirecollection of data from many different elements. A system for datacollection may be configured to sense and collect data that may becombined with structural engineering data to facilitate ODSV for thistype of industrial fan.

Referring to FIG. 48, an embodiment of a system for data collection inan industrial environment that performs coordinated data collectionsuitable for ODSV is depicted. A system for data collection in anindustrial environment may include an ODSV data collection templaterepository 7800 in which ODSV templates 7810 for data collection systemconfiguration and collection of data may be stored and accessed by asystem for a data collection controller 7802. The templates 7810 mayinclude data collection system configuration 7804 and operationinformation 7806 that may identify sensors, collectors, signal paths,reference signal information, information for initiation andcoordination of collection, and the like. The controller 7802 mayreceive an indication, such as a command from an ODSV analysis facility7808 to select and implement a specific ODSV template 7810. Thecontroller 7802 may access the template 7810 and configure the datacollection system resources based on the information in that template.In embodiments, the template may identify specific sensors,multiplexer/switch configuration, reference signals for coordinatingdata collection, data collection trigger/initiation signals and/orconditions, time duration, and/or amount of data for collection,destination of collected data, intermediate processing, if any, and anyother useful information (e.g., instance identifier, and the like). Thecontroller 7802 may configure and operate the data collection system toperform the collection for the ODSV template and optionally return thesystem configuration to a previous configuration.

An example method of data collection for performing ODSV in anindustrial environment includes automatically configuring local andremote data collection resources and collecting data from a number ofsensors using the configured resources, where the number of sensorsinclude a group of sensors that produce data that is required to performthe ODSV. In certain further embodiments, an example method furtherincludes where the sensors are distributed throughout structuralportions of an industrial machine in the industrial environment; wherethe sensors sense a range of system conditions including vibration,rotation, balance, and/or friction; where the automatically configuringis in response to a condition in the environment being detected outsideof an acceptable range of condition values; where the condition issensed by a sensor in a group of system sensors; where automaticallyconfiguring includes configuring a signal switching resource toconcurrently connect a portion of the group of sensors to datacollection resources; and/or where the signal switching resource isconfigured to maintain a connection between a reference sensor and thedata collection resources throughout a period of collecting data fromthe sensors to perform ODSV.

An example method of data collection in an industrial environmentincludes configuring a data collection plan to collect data from anumber of system sensors distributed throughout a machine in theindustrial environment, the plan based on machine structural informationand an indication of data needed to produce an ODSV of the machine;configuring data sensing, routing and collection resources in theenvironment based on the data collection plan; and collecting data basedon the data collection plan. In certain further embodiments, an examplemethod further includes: producing the ODSV; where the configuring datasensing, routing, and collection resources is in response to a conditionin the environment being detected outside of an acceptable range ofcondition values; where the condition is sensed by a sensor identifiedin the data collection plan; where configuring resources includesconfiguring a signal switching resource to concurrently connect theplurality of system sensors to data collection resources; and/or wherethe signal switching resource is configured to maintain a connectionbetween a reference sensor and the data collection resources throughouta period of collecting data from the sensors to perform ODSV.

An example system for data collection in an industrial environmentincludes: a number of sensors disposed throughout the environment;multiplexer that connects signals from the plurality of sensors to datacollection resources; and a processor for processing data collected fromthe number of sensors in response to the data collection template, wherethe processing results in an ODSV of a portion of a machine disposed inthe environment. In certain further embodiments, an example systemincludes: where the ODSV collection template further identifies acondition in the environment on which performing data collection fromthe identified sensors is dependent; where the condition is sensed by asensor identified in the ODSV data collection template; where the datacollection template specified inputs of the multiplexer to concurrentlyconnect to data collection resources; where the multiplexer isconfigured to maintain a connection between a reference sensor and thedata collection resources throughout a period of collecting data fromthe sensors to perform ODSV; where the ODSV data collection templatespecifies data collection requirements for performing ODSV forlooseness, soft joints, bending, and/or twisting of a portion of amachine in the industrial environment; and/or where the ODSV collectiontemplate specifies an order and timing of data collection from aplurality of identified sensors.

An example method of monitoring a mining conveyer for performing ODSV ofthe conveyer includes automatically configuring local and remote datacollection resources and collecting data from a number of sensorsdisposed to sense the mining conveyor using the configured resources. Inembodiments, the plurality of sensors comprises a group of sensors thatproduce data that is required to perform the operational deflectionshape visualization of a portion of the conveyor. An example method ofmonitoring a mining fan for performing ODSV of the fan includesautomatically configuring local and remote data collection resourcescollecting data from a number of sensors disposed to sense the fan usingthe configured resources, and where the number of sensors include agroup of sensors that produce data that is sufficient or required toperform ODSV of a portion of the fan.

In embodiments, a system for data collection in an industrialenvironment may include a hierarchical multiplexer that facilitatessuccessive multiplexing of input data channels according to aconfigurable hierarchy, such as a user configurable hierarchy. Thesystem for data collection in an industrial environment may include thehierarchical multiplexer that facilitates successive multiplexing of aplurality of input data channels according to a configurable hierarchy.The hierarchy may be automatically configured by a controller based onan operational parameter in the industrial environment, such as aparameter of a machine in the industrial environment.

In embodiments, a system for data collection in an industrialenvironment may include a plurality of sensors that may output data atdifferent rates. The system may also include a multiplexer module thatreceives sensor outputs from a first portion of the plurality of sensorswith similar output rates into separate inputs of a first hierarchicalmultiplexer of the multiplexer module. The first hierarchicalmultiplexer of the multiplexer module may provide at least onemultiplexed output of a portion of its inputs to a second hierarchicalmultiplexer that receives sensor outputs from a second portion of theplurality of sensors with similar output rates and that provides atleast one multiplexed output of a portion of its inputs. In embodiments,the output rates of the first set of sensors may be slower than theoutput rates of the second set of sensors. In embodiments, datacollection rate requirements of the first set of sensors may be lowerthan the data collection rate requirements of the second set of sensors.In embodiments, the first hierarchical multiplexer output is atime-multiplexed combination of a portion of its inputs. In embodiments,the second hierarchical multiplexer receives sensor signals with outputrates that are similar to a rate of output of the first multiplexer. Inembodiments, the first multiplexer produces time-based multiplexing ofthe portion of its plurality of inputs.

In embodiments, a system for data collection in an industrialenvironment may include a hierarchical multiplexer that is dynamicallyconfigured based on a data acquisition template. The hierarchicalmultiplexer may include a plurality of inputs and a plurality ofoutputs. In embodiments, any input can be directed to any output inresponse to sensor output collection requirements of the template. Inembodiments, a subset of the inputs can be multiplexed at a firstswitching rate and output to at least one of the plurality of outputs.

In embodiments, a system for data collection in an industrialenvironment may include a plurality of sensors for sensing conditions ofa machine in the environment, a hierarchical multiplexer, a plurality ofanalog-to-digital converters (ADCs), a processor, local storage, and anexternal interface. The system may use the processor to access a dataacquisition template of parameters for data collection from a portion ofthe plurality of sensors, configure the hierarchical multiplexer, theADCs and the local storage to facilitate data collection based on thedefined parameters, and execute the data collection with the configuredelements including storing a set of data collected from a portion of theplurality of sensors into the local storage. In embodiments, the ADCsconvert analog sensor data into a digital form that is compatible withthe hierarchical multiplexer. In embodiments, the processor monitors atleast one signal generated by the sensors for a trigger condition and,upon detection of the trigger condition, responds by at least one ofcommunicating an alert over the external interface and performing dataacquisition according to a template that corresponds to the triggercondition.

In embodiments, a system for data collection in an industrialenvironment may include a hierarchical multiplexer that may beconfigurable based on a data collection template of the environment. Themultiplexer may support receiving a large number of data signals (e.g.,from sensors in the environment) simultaneously. In embodiments, allsensors for a portion of an industrial machine in the environment may beindividually connected to inputs of a first stage of the multiplexer.The first stage of the multiplexer may provide a plurality of outputsthat may feed into a second multiplexer stage. The second stagemultiplexer may provide multiple outputs that feed into a third stage,and so on. Data collection templates for the environment may beconfigured for certain data collection sets, such as a set to determinetemperature throughout a machine or a set to determine vibrationthroughout a machine, and the like. Each template may identify aplurality of sensors in the environment from which data is to becollected, such as during a data collection event. When a template ispresented to the hierarchical multiplexer, mapping of inputs to outputsfor each multiplexing stage may be configured so that the required datais available at output(s) of a final multiplexing hierarchical stage fordata collection. In an example, a data collection template to collect aset of data to determine temperature throughout a machine in theenvironment may identify many temperature sensors. The first stagemultiplexer may respond to the template by selecting all of theavailable inputs that connect to temperature sensors. The data fromthese sensors maybe multiplexed onto multiple inputs of a second stagesensor that may perform time-based multiplexing to produce atime-multiplexed output(s) of temperature data from a portion of thesensors. These outputs may be gathered by a data collector andde-multiplexed into individual sensor temperature readings.

In embodiments, time-sensitive signals, such as triggers and the like,may connect to inputs that directly connect to a final multiplexerstage, thereby reducing any potential delay caused by routing throughmultiple multiplexing stages.

In embodiments, a hierarchical multiplexer in a system for datacollection in an industrial environment may comprise an array of relays,a programmable logic component, such as a CPLD, a field programmablegate array (FPGA), and the like.

In embodiments, a system for data collection in an industrialenvironment that may include a hierarchical multiplexer for routingsensor outputs onto signal paths may be used with explosive systems inmining applications. Blast initiating and electronic blasting systemsmay be configured to provide computer assisted blasting systems.Ensuring that blasting occurs safely may involve effective sensing andanalysis of a range of conditions. A system for data collection in anindustrial environment may be deployed to sense and collect dataassociated with explosive systems, such as explosive systems used formining. A data collection system can use a hierarchical multiplexer tocapture data from explosive system installations automatically byaligning, for example, a deployment of the explosive system includingits layout plans, integration, interconnectivity, cascading plan, andthe like with the hierarchical multiplexer. An explosive system may bedeployed with a form of hierarchy that starts with a primary initiatorand follows detonation connections through successive layers ofelectronic blast control to sequenced detonation. Data collected fromeach of these layers of blast systems configuration may be associatedwith stages of a hierarchical multiplexer so that data collected frombulk explosive detonation can be captured in a hierarchy thatcorresponds to its blast control hierarchy.

In embodiments, a system for data collection in an industrialenvironment that may include a hierarchical multiplexer for routingsensor outputs onto signal paths may be used with refinery blowers inoil and gas pipeline applications. Refinery blower applications includefired heater combustion air preheat systems and the like. Forced draftblowers may include a range of moving and moveable parts that maybenefit from condition sensing and monitoring. Sensing may includedetecting conditions of: couplings (e.g., temperature, rotational rate,and the like); motors (vibration, temperature, RPMs, torque, powerusage, and the like); louver mechanics (actuators, louvers, and thelike); and plenums (flow rate, blockage, back pressure, and the like). Asystem for data collection in an industrial environment that uses ahierarchical multiplexer for routing signals from sensors and the liketo data collectors may be configured to collect data from a refineryblower. In an example, a plurality of sensors may be deployed to senseair flow into, throughout, and out of a forced draft blower used in arefinery application, such as to preheat combustion air. Sensors may begrouped based on a frequency of a signal produced by sensors. Sensorsthat detect louver position and control may produce data at a lower ratethan sensors that detect blower RPMs. Therefore, louver position andcontrol sensor signals can be applied to a lower stage in a multiplexerhierarchy than the blower RPM sensors because data from louvers changeless often than data from RPM sensors. A data collection system couldswitch among a plurality of louver sensors and still capture enoughinformation to properly detect louver position. However, properlydetecting blower RPM data may require greater bandwidth of connectionbetween the blower RPM sensor and a data collector. A hierarchicalmultiplexer may enable capturing blower RPM data at a rate that isrequired for proper detection (perhaps by outputting the RPM sensor datafor long durations of time), while switching among several louver sensorinputs and directing them onto (or through) an output that is differentthan the blower RPM output. Alternatively, the louver inputs may betime-multiplexed with the blower RPM data onto a single output that canbe de-multiplexed by a data collector that is configured to determinewhen blower RPM data is being output and when louver position data isbeing output.

In embodiments, a system for data collection in an industrialenvironment that may include a hierarchical multiplexer for routingsensor outputs onto signal paths may be used with pipeline-relatedcompressors (e.g., reciprocating) in oil and gas pipeline applications.A typical use of a reciprocating compressor for pipeline application isproduction of compressed air for pipeline testing. A system for datacollection in an industrial environment may apply a hierarchicalmultiplexer while collecting data from a pipeline testing-basedreciprocating compressor. Data from sensors deployed along a portion ofa pipeline being tested may be input to the lowest stage of thehierarchical multiplexer because these sensors may be periodicallysampled prior to and during testing. However, the rate of sampling maybe low relative to sensors that detect compressor operation, such asparts of the compressor that operate at higher frequencies, such as thereciprocating linkage, motor, and the like. The sensors that providedata at frequencies that enable reproduction of the detected motion maybe input to higher stages in the hierarchical multiplexer. Timemultiplexing among the pipeline sensors may provide for coverage of alarge number of sensors while capturing events such as seal leakage andthe like. However, time multiplexing among reciprocating linkage sensorsmay require output signal bandwidth that may exceed the bandwidthavailable for routing data from the multiplexer to a data collector.Therefore, in embodiments, a plurality of pipeline sensors may betime-multiplexed onto a single multiplexer output and a compressorsensor detecting rapidly moving parts, such as the compressor motor, maybe routed to separate outputs of the multiplexer.

Referring to FIG. 49, a system for data collection in an industrialenvironment that uses a hierarchical multiplexer for routing sensorsignals to data collectors is depicted. Outputs from a plurality ofsensors, such as sensors that monitor conditions that change withrelatively low frequency (e.g., blower louver position sensors) may beinput to a lowest hierarchical stage 8000 of a hierarchical multiplexer8002 and routed to successively higher stages in the multiplexer,ultimately being output from the multiplexer, perhaps as atime-multiplexed signal comprising time-specific samples of each of theplurality of low frequency sensors. Outputs from a second plurality ofsensors, such as sensors that monitor motor operation that may run atmore than 1000 RPMs may be input to a higher hierarchical stage 8004 ofthe hierarchical multiplexer and routed to outputs that support therequired bandwidth.

An example system for data collection in an industrial environmentincludes a controller for controlling data collection resources in theindustrial environment and a hierarchical multiplexer that facilitatessuccessive multiplexing of a number of input data channels according toa configurable hierarchy. In embodiments, the hierarchy is automaticallyconfigured by the controller based on an operational parameter of amachine in the industrial environment. In certain further embodiments,an example system includes: where the operational parameter of themachine is identified in a data collection template; where the hierarchyis automatically configured in response to smart band data collectionactivation further including an analog-to-digital converter disposedbetween a source of the input data channels and the hierarchicalmultiplexer; and/or where the operational parameter of the machinecomprises a trigger condition of at least one of the data channels.Another example system for data collection in an industrial environmentincludes a plurality of sensors and a multiplexer module that receivessensor outputs from a first portion of the sensors with similar outputrates into separate inputs of a first hierarchical multiplexer thatprovides at least one multiplexed output of a portion of its inputs to asecond hierarchical multiplexer, the second hierarchical multiplexerreceiving sensor outputs from a second portion of the sensors andproviding at least one multiplexed output of a portion of its inputs. Incertain further embodiments, an example system includes: where thesecond portion of the sensors output data at rates that are higher thanthe output rates of the first portion of the sensors; where the firstportion and the second portion of the sensors output data at differentrates; where the first hierarchical multiplexer output is atime-multiplexed combination of a portion of its inputs; where thesecond multiplexer receives sensor signals with output rates that aresimilar to a rate of output of the first multiplexer; and/or where thefirst multiplexer produces time-based multiplexing of the portion of itsinputs.

An example system for data collection in an industrial environmentincludes a number of sensors for sensing conditions of a machine in theenvironment a hierarchical multiplexer, a number of analog-to-digitalconverters, a controller, local storage, an external interface, wherethe system includes using the controller to access a data acquisitiontemplate that defines parameters for data collection from a portion ofthe sensors, to configure the hierarchical multiplexer, the ADCs, andthe local storage to facilitate data collection based on the definedparameters, and to execute the data collection with the configuredelements including storing a set of data collected from a portion of thesensors into the local storage. In certain further embodiments, anexample system includes: where the ADCs convert analog sensor data intoa digital form that is compatible with the hierarchical multiplexer;where the processor monitors at least one signal generated by thesensors for a trigger condition and, upon detection of the triggercondition, responds by communicating an alert over the externalinterface and/or performing data acquisition according to a templatethat corresponds to the trigger condition; where the hierarchicalmultiplexer performs successive multiplexing of data received from thesensors according to a configurable hierarchy; where the hierarchy isautomatically configured by the controller based on an operationalparameter of a machine in the industrial environment; where theoperational parameter of the machine is identified in a data collectiontemplate; where the hierarchy is automatically configured in response tosmart band data collection activation; the system further including anADC disposed between a source of the input data channels and thehierarchical multiplexer; where the operational parameter of the machineincludes a trigger condition of at least one of the data channels; wherethe hierarchical multiplexer performs successive multiplexing of datareceived from the plurality of sensors according to a configurablehierarchy; and/or where the hierarchy is automatically configured by acontroller based on a detected parameter of an industrial environment.Without limitation, n example system is configured for monitoring amining explosive system and includes a controller for controlling datacollection resources associated with the explosive system, and ahierarchical multiplexer that facilitates successive multiplexing of anumber of input data channels according to a configurable hierarchy,where the hierarchy is automatically configured by the controller basedon a configuration of the explosive system. Without limitation, anexample system is configured for monitoring a refinery blower in an oiland gas pipeline applications, and includes a controller for controllingdata collection resources associated with the refinery blower, and ahierarchical multiplexer that facilitates successive multiplexing of anumber of input data channels according to a configurable hierarchy,where the hierarchy is automatically configured by the controller basedon a configuration of the refinery blower. Without limitation, anexample system is configured for monitoring a reciprocating compressorin an oil and gas pipeline applications comprising, and includescontroller for controlling data collection resources associated with thereciprocating compressor, and a hierarchical multiplexer thatfacilitates successive multiplexing of a number of input data channelsaccording to a configurable hierarchy, where the hierarchy isautomatically configured by the controller based on a configuration ofthe reciprocating compressor.

In embodiments, a system for data collection in an industrialenvironment may include an ultrasonic sensor disposed to captureultrasonic conditions of an element of in the environment. The systemmay be configured to collect data representing the captured ultrasoniccondition in a computer memory, on which a processor may execute anultrasonic analysis algorithm. In embodiments, the sensed element may beone of a moving element, a rotating element, a structural element, andthe like. In embodiments, the data may be streamed to the computermemory. In embodiments, the data may be continuously streamed. Inembodiments, the data may be streamed for a duration of time, such as anultrasonic condition sampling duration. In embodiments, the system mayalso include a data routing infrastructure that facilitates routing thestreaming data from the ultrasonic sensor to a plurality of destinationsincluding local and remote destinations. The routing infrastructure mayinclude a hierarchical multiplexer that is adapted to route thestreaming data and data from at least one other sensor to a destination.

In embodiments, ultrasonic monitoring in an industrial environment maybe performed by a system for data collection as described herein onrotating elements (e.g., motor shafts and the like), bearings, fittings,couplings, housings, load bearing elements, and the like. The ultrasonicdata may be used for pattern recognition, state determination,time-series analysis, and the like, any of which may be performed bycomputing resources of the industrial environment, which may includelocal computing resources (e.g., resources located within theenvironment and/or within a machine in the environment, and the like)and remote computing resources (e.g., cloud-based computing resources,and the like).

In embodiments, ultrasonic monitoring in an industrial environment by asystem for data collection may be activated in response to a trigger(e.g., a signal from a motor indicating the motor is operational, andthe like), a measure of time (e.g., an amount of time since the mostrecent monitoring activity, a time of day, a time relative to a trigger,an amount of time until a future event, such as machine shutdown, andthe like), an external event (e.g., lightning strike, and the like). Theultrasonic monitoring may be activated in response to implementation ofa smart band data collection activity. The ultrasonic monitoring may beactivated in response to a data collection template being applied in theindustrial environment. The data collection template may be configuredbased on analysis of prior vibration-caused failures that may beapplicable to the monitored element, machine, environment, and the like.Because continuous monitoring of ultrasonic data may require dedicatingdata routing resources in the industrial environment for extendedperiods of time, a data collection template for continuous ultrasonicmonitoring may be configured with data routing and resource utilizationsetup information that a controller of a data collection system may useto setup the resources to accommodate continuous ultrasonic monitoring.In an example, a data multiplexer may be configured to dedicate aportion of its outputs to the ultrasonic data for a duration of timespecified in the template.

In embodiments, a system for data collection in an industrialenvironment may perform continuous ultrasonic monitoring. The system mayalso include processing of the ultrasonic data by a local processorlocated proximal to the vibration monitoring sensor or device(s).Depending on the computing capabilities of the local processor,functions such as peak detection may be performed. A programmable logiccomponent may provide sufficient computing capabilities to perform peakdetection. Processing of the ultrasonic data (local or remote) mayprovide feedback to a controller associated with the element(s) beingmonitored. The feedback may be used in a control loop to potentiallyadjust an operating condition, such as rotational speed, and the like,in an attempt to reduce or at least contain potential negative impactsuggested by the ultrasonic data analysis.

In embodiments, a system for data collection in an industrialenvironment may perform ultrasonic monitoring, and in particular,continuous ultrasonic monitoring. The ultrasonic monitoring data may becombined with multi-dimensional models of an element or machine beingmonitored to produce a visualization of the ultrasonic data. Inembodiments, an image, set of images, video, and the like may beproduced that correlates in time with the sensed ultrasonic data. Inembodiments, image recognition and/or analysis may be applied toultrasonic visualizations to further facilitate determining the severityof a condition detected by the ultrasonic monitoring. The image analysisalgorithms may be trained to detect normal and out of bounds conditions.Data from load sensors may be combined with ultrasonic data tofacilitate testing materials and systems.

In embodiments, a system for data collection in an industrialenvironment may perform ultrasonic monitoring of a pipeline in an oiland gas pipeline application. Flows of petroleum through pipelines cancreate vibration and other mechanical effects that may contribute tostructural changes in a liner of the pipeline, support members, flowboosters, regulators, diverters, and the like. Performing continuousultrasonic monitoring of key elements in a pipeline may facilitatedetecting early changes in material, such as joint fracturing, and thelike, that may lead to failure. A system for data collection in anindustrial environment may be configured with ultrasonic sensing devicesthat may be connected through signal data routing resources, such ascrosspoint switches, multiplexers, and the like, to data collection andanalysis nodes at which the collected ultrasonic data can be collectedand analyzed. In embodiments, a data collection system may include acontroller that may reference a data collection plan or template thatincludes information to facilitate configuring the data sampling,routing, and collection resources of the system to accommodatecollecting ultrasonic sample data from a plurality of elements along thepipeline. The template may indicate a sequence for collecting ultrasonicdata from a plurality of ultrasonic sensors and the controller mayconfigure a multiplexer to route ultrasonic sensor data from a specifiedultrasonic sensor to a destination, such as a data storage controller,analysis processor and the like, for a duration specified in thetemplate. The controller may detect a sequence of collection in thetemplate, or a sequence of templates to access, and respond to eachtemplate in the detected sequence, adjusting the multiplexer and thelike to route the sensor data specified in each template to a collector.

In embodiments, a system for data collection in an industrialenvironment may perform ultrasonic monitoring of compressors in a powergeneration application. Compressors include several critical rotatingelements (e.g., shaft, motor, and the like), rotational support elements(e.g., bearings, couplings, and the like), and the like. A system fordata collection configured to facilitate sensing, routing, collectionand analysis of ultrasonic data in a power generation application mayreceive ultrasonic sensor data from a plurality of ultrasonic sensors.Based on a configuration setup template, such as a template forcollecting continuous ultrasonic data from one or more ultrasonic sensordevices, a controller may configure resources of the data collectionsystem to facilitate delivery of the ultrasonic data over one or moresignal data lines from the sensor(s) at least to data collectors thatmay be locally or remotely accessible. In embodiments, a template mayindicate that ultrasonic data for a main shaft should be retrievedcontinuously for one minute, and then ultrasonic data for a secondaryshaft should be retrieved for another minute, followed by ultrasonicdata for a housing of the compressor. The controller may configure amultiplexer that receives the ultrasonic data for each of these sensorsto route the data from each sensor in order by configuring a control setthat initially directs the inputs from the main shaft ultrasonic sensorsthrough the multiplexer until the time or other measure of data beingforwarded is reached. The controller could switch the multiplexer toroute the additional ultrasonic data as required to satisfy the secondtemplate requirements. The controller may continue adjusting the datacollection system resources along the way until all of the ultrasonicmonitoring data collection templates are satisfied.

In embodiments, a system for data collection in an industrialenvironment may perform ultrasonic monitoring of wind turbine gearboxesin a wind energy generation application. Gearboxes in wind turbines mayexperience a high degree of resistance in operation, due in part to thechanging nature of wind, which may cause moving parts, such as the gearplanes, hydraulic fluid pumps, regulators, and the like, to prematurelyfail. A system for data collection in an industrial environment may beconfigured with ultrasonic sensors that capture information that maylead to early detection of potential failure modes of these high-strainelements. To ensure that ultrasonic data may be effectively acquiredfrom several different ultrasonic sensors with sufficient coverage tofacilitate producing an actionable ultrasonic imaging assessment, thesystem may be configured specifically to deliver sufficient data at arelatively high rate from one or more of the sensors. Routing channel(s)may be dedicated to transferring ultrasonic sensing data for a durationof time that may be specified in an ultrasonic data collection plan ortemplate. To accomplish this, a controller, such as a programmable logiccomponent, may configure a portion of a crosspoint switch and datacollectors to deliver ultrasonic data from a first set of ultrasonicsensors (e.g., those that sense hydraulic fluid flow control elements)to a plurality of data collectors. Another portion of the crosspointswitch may be configured to route additional sensor data that may beuseful for evaluating the ultrasonic data (e.g., motor on/off state,thermal condition of sensed parts, and the like) on other data channelsto data collectors where the data can be combined and analyzed. Thecontroller may reconfigure the data routing resources to enablecollecting ultrasonic data from other elements based on a correspondingdata collection template.

Referring to FIG. 50, a system for data collection in an industrialenvironment may include one or more ultrasonic sensors 8050 that mayconnect to a data collection and a routing system 8052 that may beconfigured by a controller 8054 based on an ultrasonic sensor-specificdata collection template 8056 that may be provided to the controller8054 by an ultrasonic data analysis facility 8058. The controller 8054may configure resources of the data collection system 8052 and monitorthe data collection for a duration of time based on the requirements fordata collection in the template 8056.

An example system for data collection in an industrial environmentincludes an ultrasonic sensor disposed to capture ultrasonic conditionsof an element in the environment, a controller that configures datarouting resources of the data collection system to route ultrasonic databeing captured by the ultrasonic sensor to a destination location thatis specified by an ultrasonic monitoring data collection template, and aprocessor executing an ultrasonic analysis algorithm on the data afterarrival at the destination. In certain further embodiments, an examplesystem includes: where the template defines a time interval ofcontinuous ultrasonic data capture from the ultrasonic sensor; a datarouting infrastructure that facilitates routing the streaming data fromthe ultrasonic sensor to a number of destinations including local andremote destinations; the routing infrastructure including a hierarchicalmultiplexer that is adapted to route the streaming data and data from atleast one other sensor to a destination; where the element in theenvironment includes rotating elements, bearings, fittings, couplings,housing, and/or load bearing parts; where the template defines acondition of activation of continuous ultrasonic monitoring; and/orwhere the condition of activation includes a trigger, a smart-band, atemplate, an external event, and/or a regulatory complianceconfiguration.

An example system for data collection in an industrial environmentincludes an ultrasonic sensor disposed to capture ultrasonic conditionsof an element of an industrial machine in the environment, a controllerthat configures data routing resources of the data collection system toroute ultrasonic data being captured by the ultrasonic sensor to adestination location that is specified by an ultrasonic monitoring datacollection template, and a processor executing an ultrasonic analysisalgorithm on the data after arrival at the destination. In certainembodiments, the template defines a time interval of continuousultrasonic data capture from the ultrasonic sensor; the system furtherincluding a data routing infrastructure that facilitates routing thedata from the ultrasonic sensor to a number of destinations includinglocal and remote destinations; the data routing infrastructure includinga hierarchical multiplexer that is adapted to route the ultrasonic dataand data from at least one other sensor to a destination; where theelement of the industrial machine includes rotating elements, bearings,fittings, couplings, housing, and/or load bearing parts; where thetemplate defines a condition of activation of continuous ultrasonicmonitoring; and/or where the condition of activation includes a trigger,a smart-band, a template, an external event, and/or a regulatorycompliance configuration.

An example method of continuous ultrasonic monitoring in an industrialenvironment includes disposing an ultrasonic monitoring device withinultrasonic monitoring range of at least one moving part of an industrialmachine in the industrial environment, the ultrasonic monitoring deviceproducing a stream of ultrasonic monitoring data, configuring, based onan ultrasonic monitoring data collection template, a data routinginfrastructure to route the stream of ultrasonic monitoring data to adestination, where the infrastructure facilitates routing data from anumber of sensors through an analog crosspoint switch and/or ahierarchical multiplexer, to a number of destinations, routing theultrasonic monitoring device data through the routing infrastructure toa destination; processing the stored data with an ultrasonic dataanalysis algorithm that provides an ultrasonic analysis of at least oneof a motor shaft, bearings, fittings, couplings, housing, and loadbearing parts; and/or storing the data in a computer accessible memoryat the destination. Certain further embodiments of an example methodinclude: where the data collection template defines a time interval ofcontinuous ultrasonic data capture from the ultrasonic monitoringdevice; where configuring the data routing infrastructure includesconfiguring the hierarchical multiplexer to route the ultrasonic dataand data from at least one other sensor to a destination; whereultrasonic monitoring is performed on at least one element in anindustrial machine that includes rotating elements, bearings, fittings,couplings, a housing, and/or load bearing parts; where the templatedefines a condition of activation of continuous ultrasonic monitoring;where the condition of activation includes a trigger, a smart-band, atemplate, an external event, and/or a regulatory complianceconfiguration; where the ultrasonic data analysis algorithm performspattern recognition; and/or where routing the ultrasonic monitoringdevice data is in response to detection of a condition in the industrialenvironment associated with the at least one moving part.

Without limitation, an example system for monitoring an oil or gaspipeline includes a processor executing an ultrasonic analysis algorithmon the pipeline data after arrival at the destination; an example systemfor monitoring a power generation compressor includes a processorexecuting an ultrasonic analysis algorithm on the power generationcompressor data after arrival at the destination; and an example systemfor monitoring a wind turbine gearbox includes a processor executing anultrasonic analysis algorithm on the gearbox data after arrival at thedestination.

Industrial components such as pumps, compressors, air conditioningunits, mixers, agitators, motors, and engines may play critical roles inthe operation of equipment in a variety of environments including aspart of manufacturing equipment in industrial environments such asfactories, gas handling systems, mining operations, automotive systems,and the like.

There are a wide variety of pumps such as a variety of positivedisplacement pumps, velocity pumps, and impulse pumps. Velocity orcentrifugal pumps typically comprise an impeller with curved bladeswhich, when an impeller is immersed in a fluid, such as water or a gas,causes the fluid or gas to rotate in the same rotational direction asthe impeller. As the fluid or gas rotates, centrifugal force causes itto move to the outer diameter of the pump, e.g., the pump housing, whereit can be collected and further processed. The removal of the fluid orgas from the outer circumference may result in lower pressure at a pumpinput orifice causing new fluid or gas to be drawn into the pump.

Positive displacement pumps may comprise reciprocating pumps,progressive cavity pumps, gear or screw pumps, such as reciprocatingpumps typically comprise a piston which alternately creates suction,which opens an inlet valve and draws a liquid or gas into a cylinder,and pressure, which closes the inlet valve and forces the liquid or gaspresent out of the cylinder through an outlet valve. This method ofpumping may result in periodic waves of pressurized liquid or gas beingintroduced into the downstream system.

Some automotive vehicles such as cars and trucks may use a water coolingsystem to keep the engine from overheating. In some automobiles, acentrifugal water pump, driven by a belt associated with a driveshaft ofthe vehicle, is used to force a mixture of water and coolant through theengine to maintain an acceptable engine temperature. Overheating of theengine may be highly destructive to the engine and yet it may bedifficult or costly to access a water pump installed in a vehicle.

In embodiments, a vehicle water pump may be equipped with a plurality ofsensors for measuring attributes associated with the water pump such astemperature of bearings or pump housing, vibration of a driveshaftassociated with the pump, liquid leakage, and the like. These sensorsmay be connected either directly to a monitoring device or through anintermediary device using a mix of wired and wireless connectiontechniques. A monitoring device may have access to detection valuescorresponding to the sensors where the detection values corresponddirectly to the sensor output or a processed version of the data outputsuch as a digitized or sampled version of the sensor output, and/or avirtual sensor or modeled value correlated from other sensed values. Themonitoring device may access and process the detection values usingmethods discussed elsewhere herein to evaluate the health of the waterpump and various components of the water pump prone to wear and failure,e.g., bearings or sets of bearings, drive shafts, motors, and the like.The monitoring device may process the detection values to identify atorsion of the drive shaft of the pump. The identified torsion may thenbe evaluated relative to expected torsion based on the specific geometryof the water pump and how it is installed in the vehicle. Unexpectedtorsion may put undue stress on the driveshaft and may be a sign ofdeteriorating health of the pump. The monitoring device may process thedetection values to identify unexpected vibrations in the shaft orunexpected temperature values or temperature changes in the bearings orin the housing in proximity to the bearings. In some embodiments, thesensors may include multiple temperature sensors positioned around thewater pump to identify hot spots among the bearings or across the pumphousing which might indicate potential bearing failure. The monitoringdevice may process the detection values associated with water sensors toidentify liquid leakage near the pump which may indicate a bad seal. Thedetection values may be jointly analyzed to provide insight into thehealth of the pump.

In an illustrative example, detection values associated with a vehiclewater pump may show a sudden increase in vibration at a higher frequencythan the operational rotation of the pump with a corresponding localizedincrease of temperature associated with a specific phase in the pumpcycle. Together these may indicate a localized bearing failure.

Production lines may also include one or more pumps for moving a varietyof material including acidic or corrosive materials, flammablematerials, minerals, fluids comprising particulates of varying sizes,high viscosity fluids, variable viscosity fluids, or high-densityfluids. Production line pumps may be designed to specifically meet theneeds of the production line including pump composition to handle thevarious material types, or torque needed to move the fluid at thedesired speed or with the desired pressure. Because these productionlines may be continuous process lines, it may be desirable to performproactive maintenance rather than wait for a component to fail.Variations in pump speed and pressure may have the potential tonegatively impact the final product, and the ability to identify issuesin the final product may lag the actual component deterioration by anunacceptably long period.

In embodiments, an industrial pump may be equipped with a plurality ofsensors for measuring attributes associated with the pump such astemperature of bearings or pump housing, vibration of a driveshaftassociated with the pump, vibration of input or output lines, pressure,flow rate, fluid particulate measures, vibrations of the pump housing,and the like. These sensors may be connected either directly to amonitoring device or through an intermediary device using a mix of wiredand wireless connection techniques. A monitoring device may have accessto detection values corresponding to the sensors where the detectionvalues correspond directly to the sensor output of a processed versionof the data output such as a digitized or sampled version of the sensoroutput. The monitoring device may access and process the detectionvalues using methods discussed elsewhere herein to evaluate the healthof the pump overall, evaluate the health of pump components, predictpotential down line issues arising from atypical pump performance, orchanges in fluid being pumped. The monitoring device may process thedetection values to identify torsion on the drive shaft of the pump. Theidentified torsion may then be evaluated relative to expected torsionbased on the specific geometry of the pump and how it is installed inthe equipment relative to other components on the assembly line.Unexpected torsion may put undue stress on the driveshaft and may be asign of deteriorating health of the pump. Vibration of the inlet andoutlet pipes may also be evaluated for unexpected or resonant vibrationswhich may be used to drive process controls to avoid certain pumpfrequencies. Changes in vibration may also be due to changes in fluidcomposition or density, amplifying or dampening vibrations at certainfrequencies. The monitoring device may process the detection values toidentify unexpected vibrations in the shaft, unexpected temperaturevalues, or temperature changes in the bearings or in the housing inproximity to the bearings. In some embodiments, the sensors may includemultiple temperature sensors positioned around the pump to identify hotspots among the bearings or across the pump housing which mightindicated potential bearing failure. For some pumps, when the fluidbeing pumped is corrosive or contains large amounts of particulates,there may be damage to the interior components of the pump in contactwith the fluid due to cumulative exposure to the fluid. This may bereflected in unanticipated variations in output pressure. Additionallyor alternatively, if a gear in a gear pump begins to corrode and nolonger forces all the trapped fluid out this may result in increasedpump speed, fluid cavitation, and/or unexpected vibrations in the outputpipe.

Compressors increase the pressure of a gas by decreasing the volumeoccupied by the gas or increasing the amount of the gas in a confinedvolume. There may be positive-displacement compressors that utilize themotion of pistons or rotary screws to move the gas into a pressurizedholding chamber. There are dynamic displacement gas compressors that usecentrifugal force to accelerate the gas into a stationary compressorwhere the kinetic energy is converted to pressure. Compressors may beused to compress various gases for use on an assembly line. Compressedair may power pneumatic equipment on an assembly line. In the oil andgas industry, flash gas compressors may be used to compress gas so thatit leaves a hydrocarbon liquid when it enters a lower pressureenvironment. Compressors may be used to restore pressure in gas and oilpipelines, to mix fluids of interest, and/or to transfer or transportfluids of interest. Compressors may be used to enable the undergroundstorage of natural gas.

Like pumps, compressors may be equipped with a plurality of sensors formeasuring attributes associated with the compressor such as temperatureof bearings or compressor housing, vibration of a driveshaft,transmission, gear box and the like associated with the compressor,vessel pressure, flow rate, and the like. These sensors may be connectedeither directly to a monitoring device or through an intermediary deviceusing a mix of wired and wireless connection techniques. A monitoringdevice may have access to detection values corresponding to the sensorswhere the detection values correspond directly to the sensor output of aprocessed version of the data output such as a digitized or sampledversion of the sensor output. The monitoring device may access andprocess the detection values using methods described elsewhere herein toevaluate the health of the compressor overall, evaluate the health ofcompressor components and/or predict potential down line issues arisingfrom atypical compressor performance. The monitoring device may processthe detection values to identify torsion on a driveshaft of thecompressor. The identified torsion may then be evaluated relative toexpected torsion based on the specific geometry of the compressor andhow it is installed in the equipment relative to other components andpieces of equipment. Unexpected torsion may put undue stress on thedriveshaft and may be a sign of deteriorating health of the compressor.Vibration of the inlet and outlet pipes may also be evaluated forunexpected or resonant vibrations which may be used to drive processcontrols to avoid certain compressor frequencies. The monitoring devicemay process the detection values to identify unexpected vibrations inthe shaft, unexpected temperature values or temperature changes in thebearings or in the housing in proximity to the bearings. In someembodiments, the sensors may include multiple temperature sensorspositioned around the compressor to identify hot spots among thebearings or across the compressor housing, which might indicatepotential bearing failure. In some embodiments, sensors may monitor thepressure in a vessel storing the compressed gas. Changes in the pressureor rate of pressure change may be indicative of problems with thecompressor.

Agitators and mixers are used in a variety of industrial environments.Agitators may be used to mix together different components such asliquids, solids, or gases. Agitators may be used to promote a morehomogenous mixture of component materials. Agitators may be used topromote a chemical reaction by increasing exposure between differentcomponent materials and adding energy to the system. Agitators may beused to promote heat transfer to facilitate uniform heating or coolingof a material.

Mixers and agitators are used in such diverse industries as chemicalproduction, food production, pharmaceutical production, and the like.There are paint and coating mixers, adhesive and sealant mixers, oil andgas mixers, water treatment mixers, wastewater treatment mixers, and thelike.

Agitators may comprise equipment that rotates or agitates an entire tankor vessel in which the materials to be mixed are located, such as aconcrete mixer. Effective agitations may be influenced by the number andshape of baffles in the interior of the tank. Agitation by rotation ofthe tank or vessel may be influenced by the axis of rotation relative tothe shape of the tank, direction of rotation, and external forces suchas gravity acting on the material in the tank. Factors affecting theefficacy of material agitation or mixing by agitation of the tank orvessel may include axes of rotation, and amplitude and frequency ofvibration along different axes. These factors may be selected based onthe types of materials being selected, their relative viscosities,specific gravities, particulate count, any shear thinning or shearthickening anticipated for the component materials or mixture, flowrates of material entering or exiting the vessel or tank, direction andlocation of flows of material entering of exiting the vessel, and thelike.

Agitators, large tank mixers, portable tank mixers, tote tank mixers,drum mixers, and mounted mixers (with various mount types) may comprisea propeller or other mechanical device such as a blade, vane, or statorinserted into a tank of materials to be mixed, while rotating apropeller or otherwise moving a mechanical device. These may includeairfoil impellers, fixed pitch blade impellers, variable pitch bladeimpellers, anti-ragging impellers, fixed radial blade impellers,marine-type propellers, collapsible airfoil impellers, collapsiblepitched blade impellers, collapsible radial blade impellers, andvariable pitch impellers. Agitators may be mounted such that themechanical agitation is centered in the tank. Agitators may be mountedsuch that they are angled in a tank or are vertically or horizontallyoffset from the center of the vessel. The agitators may enter the tankfrom above, below, or the side of the tank. There may be a plurality ofagitators in a single tank to achieve uniform mixing throughout the tankor container of chemicals.

Agitators may include the strategic flow or introduction of componentmaterials into the vessel including the location and direction of entry,rate of entry, pressure of entry, viscosity of material, specificgravity of the material, and the like.

Successful agitation of mixing of materials may occur with a combinationof techniques such as one or more propellers in a baffled tank wherecomponents are being introduced at different locations and at differentrates.

In embodiments, an industrial mixer or agitator may be equipped with aplurality of sensors for measuring attributes associated with theindustrial mixer such as: temperature of bearings or tank housing,vibration of driveshafts associated with a propeller or other mechanicaldevice such as a blade, vane or stator, vibration of input or outputlines, pressure, flow rate, fluid particulate measures, vibrations ofthe tank housing and the like. These sensors may be connected eitherdirectly to a monitoring device or through an intermediary device usinga mix of wired and wireless connection techniques. A monitoring devicemay have access to detection values corresponding to the sensors wherethe detection values correspond directly to the sensor output of aprocessed version of the data, output such as a digitized or sampledversion of the sensor output, fusion of data from multiple sensors, andthe like. The monitoring device may access and process the detectionvalues using methods discussed elsewhere herein to evaluate the healthof the agitator or mixer overall, evaluate the health of agitator ormixer components, predict potential down line issues arising fromatypical performance or changes in composition of material beingagitated. For example, the monitoring device may process the detectionvalues to identify torsion on the driveshaft of an agitating impeller.The identified torsion may then be evaluated relative to expectedtorsion based on the specific geometry of the agitator and how it isinstalled in the equipment relative to other components and/or pieces ofequipment. Unexpected torsion may put undue stress on the driveshaft andmay be a sign of deteriorating health of the agitator. Vibration ofinflow and outflow pipes may be monitored for unexpected or resonantvibrations which may be used to drive process controls to avoid certainagitation frequencies. Inflow and outflow pipes may also be monitoredfor unexpected flow rates, unexpected particulate content, and the like.Changes in vibration may also be due to changes in fluid composition, ordensity amplifying or dampening vibrations at certain frequencies. Themonitoring device may distribute sensors to collect detection valueswhich may be used to identify unexpected vibrations in the shaft, orunexpected temperature values or temperature changes in the bearings orin the housing in proximity to the bearings. For some agitators, whenthe fluid being agitated is corrosive or contains large amounts ofparticulates, there may be damage to the interior components of theagitator (e.g., baffles, propellers, blades, and the like) which are incontact with the materials, due to cumulative exposure to the materials.

HVAC, air-conditioning systems, and the like may use a combination ofcompressors and fans to cool and circulate air in industrialenvironments. Similar to the discussion of compressors and agitators,these systems may include a number of rotating components whose failureor reduced performance might negatively impact the working environmentand potentially degrade product quality. A monitoring device may be usedto monitor sensors measuring various aspects of the one or more rotatingcomponents, the venting system, environmental conditions, and the like.Components of the HVAC/air-conditioning systems may include fan motors,driveshafts, bearings, compressors, and the like. The monitoring devicemay access and process the detection values corresponding to the sensoroutputs according to methods discussed elsewhere herein to evaluate theoverall health of the air-conditioning unit, HVAC system, and like aswell as components of these systems, identify operational states,predict potential issues arising from atypical performance, and thelike. Evaluation techniques may include bearing analysis, torsionalanalysis of driveshafts, rotors and stators, peak value detection, andthe like. The monitoring device may process the detection values toidentify issues such as torsion on a driveshaft, potential bearingfailures, and the like.

Assembly line conveyors may comprise a number of moving and rotatingcomponents as part of a system for moving material through amanufacturing process. These assembly line conveyors may operate over awide range of speeds. These conveyances may also vibrate at a variety offrequencies as they convey material horizontally to facilitatescreening, grading, lining for packaging, spreading, dewatering, feedingproduct into the next in-line process, and the like.

Conveyance systems may include engines or motors, one or moredriveshafts turning rollers or bearings along which a conveyor belt maymove. A vibrating conveyor may include springs and a plurality ofvibrators which vibrate the conveyor forward in a sinusoidal manner.

In embodiments, conveyors and vibrating conveyors may be equipped with aplurality of sensors for measuring attributes associated with theconveyor such as temperature of bearings, vibration of driveshafts,vibrations of rollers along which the conveyor travels, velocity andspeed associated with the conveyor, and the like. The monitoring devicemay access and process the detection values using methods discussedelsewhere herein to evaluate the overall health of the conveyor as wellas components of the conveyor, predict potential issues arising fromatypical performance, and the like. Techniques for evaluating theconveyors may include bearing analysis, torsional analysis, phasedetection/phase lock loops to align detection values from differentparts of the conveyor, frequency transformations and frequency analysis,peak value detection, and the like. The monitoring device may processthe detection values to identify torsion on a driveshaft, potentialbearing failures, uneven conveyance and like.

In an illustrative example, a paper-mill conveyance system may comprisea mesh onto which the paper slurry is coated. The mesh transports theslurry as liquid evaporates and the paper dries. The paper may then bewound onto a core until the roll reaches diameters of up to threemeters. The transport speeds of the paper-mill range from traditionalequipment operating at 14-48 meters/minute to new, high-speed equipmentoperating at close to 2000 meters/minute. For slower machines, the papermay be winding onto the roll at 14 meters/minute which, towards the endof the roll having a diameter of approximately three meters wouldindicate that the take up roll may be rotating at speeds on the order ofa couple of rotations a minute. Vibrations in the web conveyance ortorsion across the take up roller may result in damage to the paper,skewing of the paper on the web, or skewed rolls which may result inequipment downtime or product that is lower in quality or unusable.Additionally, equipment failure may result in costly machine shutdownsand loss of product. Therefore, the ability to predict problems andprovide preventative maintenance and the like may be useful.

Monitoring truck engines and steering systems to facilitate timelymaintenance and avoid unexpected breakdowns may be important. Health ofthe combustion chamber, rotating crankshafts, bearings, and the like maybe monitored using a monitoring device structured to interpret detectionvalues received from a plurality of sensors measuring a variety ofcharacteristics associated with engine components including temperature,torsion, vibration, and the like. As discussed above, the monitoringdevice may process the detection values to identify engine bearinghealth, torsional vibrations on a crankshaft/driveshaft, unexpectedvibrations in the combustion chambers, overheating of differentcomponents, and the like. Processing may be done locally or data may becollected across a number of vehicles and jointly analyzed. Themonitoring device may process detection values associated with theengine, combustion chambers, and the like. Sensors may monitortemperature, vibration, torsion, acoustics, and the like to identifyissues. A monitoring device or system may use techniques such as peakdetection, bearing analysis, torsion analysis, phase detection, PLL,band pass filtering, and the like to identify potential issues with thesteering system and bearing and torsion analysis to identify potentialissues with rotating components on the engine. This identification ofpotential issues may be used to schedule timely maintenance, reduceoperation prior to maintenance, and influence future component design.

Drilling machines and screwdrivers in the oil and gas industries may besubjected to significant stresses. Because they are frequently situatedin remote locations, an unexpected breakdown may result in extended downtime due to the lead-time associated with bringing in replacementcomponents. The health of a drilling machine or screwdriver andassociated rotating crankshafts, bearings, and the like may be monitoredusing a monitoring device structured to interpret detection valuesreceived from a plurality of sensors measuring a variety ofcharacteristics associated with the drilling machine or screwdriverincluding temperature, torsion, vibration, rotational speed, verticalspeed, acceleration, image sensors, and the like. As discussed above,the monitoring device may process the detection values to identifyequipment health, torsional vibrations on a crankshaft/driveshaft,unexpected vibrations in the component, overheating of differentcomponents, and the like. Processing may be done locally or datacollected across a number of machines and jointly analyzed. Themonitoring device may jointly process detection values, equipmentmaintenance records, product records, historical data, and the like toidentify correlations between detection values, current and futurestates of the component, anticipated lifetime of the component or pieceof equipment, and the like. Sensors may monitor temperature, vibration,torsion, acoustics, and the like to identify issues such asunanticipated torsion in the drill shaft, slippage in the gears,overheating, and the like. A monitoring device or system may usetechniques such as peak detection, bearing analysis, torsion analysis,phase detection, PLL, band pass filtering, and the like to identifypotential issues. This identification of potential issues may be used toschedule timely maintenance, order new or replacement components, reduceoperation prior to maintenance, and influence future component design.

Similarly, it may be desirable to monitor the health of gearboxesoperating in an oil and gas field. A monitoring device may be structuredto interpret detection values received from a plurality of sensorsmeasuring a variety of characteristics associated with the gearbox suchas temperature, vibration, and the like. The monitoring device mayprocess the detection values to identify gear and gearbox health andanticipated life. Processing may be done locally or data collectedacross a number of gearboxes and jointly analyzed. The monitoring devicemay jointly process detection values, equipment maintenance records,product records historical data, and the like to identify correlationsbetween detection values, current and future states of the gearbox,anticipated lifetime of the gearbox and associated components, and thelike. A monitoring device or system may use techniques such as peakdetection, bearing analysis, torsion analysis, phase detection, PLL,band pass filtering, to identify potential issues. This identificationof potential issues may be used to schedule timely maintenance, ordernew or replacement components, reduce operation prior to maintenance,and influence future equipment design.

Refining tanks in the oil and gas industries may be subjected tosignificant stresses due to the chemical reactions occurring inside.Because a breach in a tank could result in the release of potentiallytoxic chemicals, it may be beneficial to monitor the condition of therefining tank and associated components. Monitoring a refining tank tocollect a variety of ongoing data may be used to predict equipment wear,component wear, unexpected stress, and the like. Given predictions aboutequipment health, such as the status of a refining tank, may be used toschedule timely maintenance, order new or replacement components, reduceoperation prior to maintenance, and influence future component design.Similar to the discussion above, a refining tank may be monitored usinga monitoring device structured to interpret detection values receivedfrom a plurality of sensors measuring a variety of characteristicsassociated with the refining tank such as temperature, vibration,internal and external pressure, the presence of liquid or gas at seamsand ports, and the like. The monitoring device may process the detectionvalues to identify equipment health, unexpected vibrations in the tank,overheating of the tank or uneven heating across the tank, and the like.Processing may be done locally or data collected across a number oftanks and jointly analyzed. The monitoring device may jointly processdetection values, equipment maintenance records, product recordshistorical data, and the like to identify correlations between detectionvalues, current and future states of the tank, anticipated lifetime ofthe tank and associated components, and the like. A monitoring device orsystem may use techniques such as peak detection, bearing analysis,torsion analysis, phase detection, PLL, band pass filtering, and thelike to identify potential issues.

Similarly, it may be desirable to monitor the health of centrifugesoperating in an oil and gas refinery. A monitoring device may bestructured to interpret detection values received from a plurality ofsensors measuring a variety of characteristics associated with thecentrifuge such as temperature, vibration, pressure, and the like. Themonitoring device may process the detection values to identify equipmenthealth, unexpected vibrations in the centrifuge, overheating, pressureacross the centrifuge, and the like. Processing may be done locally ordata collected across a number of centrifuges and jointly analyzed. Themonitoring device may jointly process detection values, equipmentmaintenance records, product records historical data, and the like toidentify correlations between detection values, current and futurestates of the centrifuge, anticipated lifetime of the centrifuge andassociated components, and the like. A monitoring device or system mayuse techniques such as peak detection, bearing analysis, torsionanalysis, phase detection, PLL, band pass filtering, to identifypotential issues. This identification of potential issues may be used toschedule timely maintenance, order new or replacement components, reduceoperation prior to maintenance and influence future equipment design.

In embodiments, information about the health or other status or stateinformation of or regarding a component or piece of industrial equipmentmay be obtained by monitoring the condition of various componentsthroughout a process. Monitoring may include monitoring the amplitude ofa sensor signal measuring attributes such as temperature, humidity,acceleration, displacement, and the like. An embodiment of a datamonitoring device 8100 is shown in FIG. 51 and may include a pluralityof sensors 8106 communicatively coupled to a controller 8102. Thecontroller 8102 may include a data acquisition circuit 8104, a dataanalysis circuit 8108, a MUX control circuit 8114, and a responsecircuit 8110. The data acquisition circuit 8104 may include the MUX 8112where the inputs correspond to a subset of the detection values. The MUXcontrol circuit 8114 may be structured to provide adaptive scheduling ofthe logical control of the MUX and the correspondence of MUX input anddetected values based on a subset of the plurality of detection valuesand/or a command from the response circuit 8110 and/or the output of thedata analysis circuit 8104. The data analysis circuit 8108 may compriseone or more of a peak detection circuit, a phase differential circuit, aPLL circuit, a bandpass filter circuit, a frequency transformationcircuit, a frequency analysis circuit, a torsional analysis circuit, abearing analysis circuit, an overload detection circuit, a sensor faultdetection circuit, a vibrational resonance circuit for theidentification of unfavorable interaction among machines or components,a distortion identification circuit for the identification ofunfavorable distortions such as deflections shapes upon operation,overloading of weight, excessive forces, stress and strain-basedeffects, and the like. The data analysis circuit 8108 may output acomponent health status as a result of the analysis.

The data analysis circuit 8108 may determine a state, condition, orstatus of a component, part, sub-system, or the like of a machine,device, system or item of equipment (collectively referred to herein asa component health status) based on a maximum value of a MUX output fora given input or a rate of change of the value of a MUX output for agiven input. The data analysis circuit 8108 may determine a componenthealth status based on a time integration of the value of a MUX for agiven input. The data analysis circuit 8108 may determine a componenthealth status based on phase differential of MUX output relative to anon-board time or another sensor. The data analysis circuit 8108 maydetermine a component health status based on a relationship of value,phase, phase differential, and rate of change for MUX outputscorresponding to one or more input detection values. The data analysiscircuit 8108 may determine a component health status based on processstage or component specification or component anticipated state.

The multiplexer control circuit 8114 may adapt the scheduling of thelogical control of the multiplexer based on a component health status,an anticipated component health status, the type of component, the typeof equipment being measured, an anticipated state of the equipment, aprocess stage (different parameters/sensor values) may be important atdifferent stages in a process. The multiplexer control circuit 8114 mayadapt the scheduling of the logical control of the multiplexer based ona sequence selected by a user or a remote monitoring application, or onthe basis of a user request for a specific value. The multiplexercontrol circuit 8114 may adapt the scheduling of the logical control ofthe multiplexer based on the basis of a storage profile or plan (such asbased on type and availability of storage elements and parameters asdescribed elsewhere in this disclosure and in the documents incorporatedherein by reference), network conditions or availability (also asdescribed elsewhere in this disclosure and in the documents incorporatedherein by reference), or value or cost of component or equipment.

The plurality of sensors 8106 may be wired to ports on the dataacquisition circuit 8104. The plurality of sensors 8106 may bewirelessly connected to the data acquisition circuit 8104. The dataacquisition circuit 8104 may be able to access detection valuescorresponding to the output of at least one of the plurality of sensors8106 where the sensors 8106 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.

The selection of the plurality of sensors 8106 for the data monitoringdevice 8100 designed for a specific component or piece of equipment maydepend on a variety of considerations such as accessibility forinstalling new sensors, incorporation of sensors in the initial design,anticipated operational and failure conditions, resolution desired atvarious positions in a process or plant, reliability of the sensors, andthe like. The impact of a failure, time response of a failure (e.g.,warning time and/or off-nominal modes occurring before failure),likelihood of failure, and/or sensitivity required, and/or difficulty todetect failure conditions may drive the extent to which a component orpiece of equipment is monitored with more sensors, and/or highercapability sensors being dedicated to systems where unexpected orundetected failure would be costly or have severe consequences.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating, and the like, thesensors 8106 may comprise one or more of, without limitation, avibration sensor, a thermometer, a hygrometer, a voltage sensor and/or acurrent sensor (for the component and/or other sensors measuring thecomponent), an accelerometer, a velocity detector, a light orelectromagnetic sensor (e.g., determining temperature, composition,and/or spectral analysis, and/or object position or movement), an imagesensor, a structured light sensor, a laser-based image sensor, a thermalimager, an acoustic wave sensor, a displacement sensor, a turbiditymeter, a viscosity meter, an axial load sensor, a radial load sensor, atri-axial sensor, an accelerometer, a speedometer, a tachometer, a fluidpressure meter, an air flow meter, a horsepower meter, a flow ratemeter, a fluid particle detector, an optical (laser) particle counter,an ultrasonic sensor, an acoustical sensor, a heat flux sensor, agalvanic sensor, a magnetometer, a pH sensor, and the like, including,without limitation, any of the sensors described throughout thisdisclosure and the documents incorporated by reference.

The sensors 8106 may provide a stream of data over time that has a phasecomponent, such as relating to acceleration or vibration, allowing forthe evaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors8106 may provide a stream of data that is not conventionallyphase-based, such as temperature, humidity, load, and the like. Thesensors 8106 may provide a continuous or near continuous stream of dataover time, periodic readings, event-driven readings, and/or readingsaccording to a selected interval or schedule.

The sensors 8106 may monitor components such as bearings, sets ofbearings, motors, driveshafts, pistons, pumps, conveyors, vibratingconveyors, compressors, drills, and the like in vehicles, oil and gasequipment in the field, in assembly line components, and the like.

In embodiments, as illustrated in FIG. 51, the sensors 8106 may be partof the data monitoring device 8100, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 52 and 53, oneor more external sensors 8126, which are not explicitly part of amonitoring device 8120 but rather are new, previously attached to orintegrated into the equipment or component, may be opportunisticallyconnected to, or accessed by the monitoring device 8120. The monitoringdevice 8120 may include a controller 8122. The controller 8122 mayinclude the data acquisition circuit 8104, the data analysis circuit8108, the MUX control circuit 8114, and the response circuit 8110. Thedata acquisition circuit 8104 may comprise a MUX 8112 where the inputscorrespond to a subset of the detection values. The MUX control circuit8114 may be structured to provide the logical control of the MUX and thecorrespondence of MUX input and detected values based on a subset of theplurality of detection values and/or a command from the response circuit8110 and/or the output of the data analysis circuit 8108. The dataanalysis circuit 8108 may comprise one or more of a peak detectioncircuit, a phase differential circuit, a PLL circuit, a bandpass filtercircuit, a frequency transformation circuit, a frequency analysiscircuit, a torsional analysis circuit, a bearing analysis circuit, anoverload detection circuit, vibrational resonance circuit for theidentification of unfavorable interaction among machines or components,a distortion identification circuit for the identification ofunfavorable distortions such as deflections shapes upon operation,stress and strain-based effects, and the like.

The one or more external sensors 8126 may be directly connected to theone or more input ports 8128 on the data acquisition circuit 8104 of thecontroller 8122 or may be accessed by the data acquisition circuit 8104wirelessly, such as by a reader, interrogator, or other wirelessconnection, such as over a short-distance wireless protocol. Inembodiments, as shown in FIG. 53, the data acquisition circuit 8104 mayfurther comprise a wireless communication circuit 8130. The dataacquisition circuit 8104 may use the wireless communication circuit 8130to access detection values corresponding to the one or more externalsensors 8126 wirelessly or via a separate source or some combination ofthese methods.

In embodiments, as illustrated in FIG. 54, the controller 8134 mayfurther comprise a data storage circuit 8136. The data storage circuit8136 may be structured to store one or more of sensor specifications,component specifications, anticipated state information, detectedvalues, multiplexer output, component models, and the like. The datastorage circuit 8136 may provide specifications and anticipated stateinformation to the data analysis circuit 8108.

In embodiments, the response circuit 8110 may initiate a variety ofactions based on the sensor status provided by the data analysis circuit8108. The response circuit 8110 may adjust a sensor scaling value (e.g.,from 100 mV/gram to 10 mV/gram). The response circuit 8110 may select analternate sensor from a plurality available. The response circuit 8110may acquire data from a plurality of sensors of different ranges. Theresponse circuit 8110 may recommend an alternate sensor. The responsecircuit 8110 may issue an alarm or an alert.

In embodiments, the response circuit 8110 may cause the data acquisitioncircuit 8104 to enable or disable the processing of detection valuescorresponding to certain sensors based on the component status. This mayinclude switching to sensors having different response rates,sensitivity, ranges, and the like; accessing new sensors or types ofsensors, accessing data from multiple sensors, and the like. Switchingmay be undertaken based on a model, a set of rules, or the like. Inembodiments, switching may be under control of a machine learningsystem, such that switching is controlled based on one or more metricsof success, combined with input data, over a set of trials, which mayoccur under supervision of a human supervisor or under control of anautomated system. Switching may involve switching from one input port toanother (such as to switch from one sensor to another). Switching mayinvolve altering the multiplexing of data, such as combining differentstreams under different circumstances. Switching may involve activatinga system to obtain additional data, such as moving a mobile system (suchas a robotic or drone system), to a location where different oradditional data is available, such as positioning an image sensor for adifferent view or positioning a sonar sensor for a different directionof collection, or to a location where different sensors can be accessed,such as moving a collector to connect up to a sensor at a location in anenvironment by a wired or wireless connection. This switching may beimplemented by directing changes to the multiplexer (MUX) controlcircuit 8114.

In embodiments, the response circuit 8110 may make recommendations forthe replacement of certain sensors in the future with sensors havingdifferent response rates, sensitivity, ranges, and the like. Theresponse circuit 8110 may recommend design alterations for futureembodiments of the component, the piece of equipment, the operatingconditions, the process, and the like.

In embodiments, the response circuit 8110 may recommend maintenance atan upcoming process stop or initiate a maintenance call where themaintenance may include the replacement of the sensor with the same oran alternate type of sensor having a different response rate,sensitivity, range, and the like. In embodiments, the response circuit8110 may implement or recommend process changes—for example to lower theutilization of a component that is near a maintenance interval,operating off-nominally, or failed for purpose but is still at leastpartially operational, to change the operating speed of a component(such as to put it in a lower-demand mode), to initiate amelioration ofan issue (such as to signal for additional lubrication of a rollerbearing set, or to signal for an alignment process for a system that isout of balance), and the like.

In embodiments, the data analysis circuit 8108 and/or the responsecircuit 8110 may periodically store certain detection values and/or theoutput of the multiplexers and/or the data corresponding to the logiccontrol of the MUX in the data storage circuit 8136 to enable thetracking of component performance over time. In embodiments, based onsensor status, as described elsewhere herein, recently measured sensordata and related operating conditions such as RPMs, component loads,temperatures, pressures, vibrations, or other sensor data of the typesdescribed throughout this disclosure in the data storage circuit 8136enable the backing out of overloaded/failed sensor data. The signalevaluation circuit 8108 may store data at a higher data rate for greatergranularity in future processing, the ability to reprocess at differentsampling rates, and/or to enable diagnosing or post-processing of systeminformation where operational data of interest is flagged, and the like.

In embodiments, as shown in FIGS. 55, 56, 57, and 58, a data monitoringsystem 8138 may include at least one data monitoring device 8140. The atleast one data monitoring device 8140 may include the sensors 8106 and acontroller 8142 comprising the data acquisition circuit 8104, the dataanalysis circuit 8108, the data storage circuit 8136, and acommunication circuit 8146 to allow data and analysis to be transmittedto a monitoring application 8150 on a remote server 8148. The signalevaluation circuit 8108 may include at least an overload detectioncircuit (e.g., reference FIGS. 101 and 102) and/or a sensor faultdetection circuit (e.g., reference FIGS. 101 and 102). The signalevaluation circuit 8108 may periodically share data with thecommunication circuit 8146 for transmittal to the remote server 8148 toenable the tracking of component and equipment performance over time andunder varying conditions by the monitoring application 8150. Based onthe sensor status, the signal evaluation circuit 8108 and/or responsecircuit 8110 may share data with the communication circuit 8146 fortransmittal to the remote server 8148 based on the fit of data relativeto one or more criteria. Data may include recent sensor data andadditional data such as RPMs, component loads, temperatures, pressures,vibrations, and the like for transmittal. The signal evaluation circuit8108 may share data at a higher data rate for transmittal to enablegreater granularity in processing on the remote server.

In embodiments, as shown in FIG. 55, the communication circuit 8146 maycommunicate data directly to the remote server 8148. In embodiments, asshown in FIG. 56, the communication circuit 8146 may communicate data toan intermediate computer 8152 which may include a processor 8154 runningan operating system 8156 and a data storage circuit 8158.

In embodiments as illustrated in FIGS. 57 and 58, a data collectionsystem 8160 may have a plurality of monitoring devices 8144 collectingdata on multiple components in a single piece of equipment, collectingdata on the same component across a plurality of pieces of equipment,(both the same and different types of equipment) in the same facility,as well as collecting data from monitoring devices in multiplefacilities. The monitoring application 8150 on the remote server 8148may receive and store one or more of detection values, timing signals,and data coming from a plurality of the various monitoring devices 8144.

In embodiments, as shown in FIG. 57, the communication circuit 8146 maycommunicate data directly to the remote server 8148. In embodiments, asshown in FIG. 58, the communication circuit 8146 may communicate data tothe intermediate computer 8152 which may include the processor 8154running the operating system 8156 and the data storage circuit 8158.There may be an individual intermediate computer 8152 associated witheach monitoring device 8140 or an individual intermediate computer 8152may be associated with a plurality of monitoring devices 8144 where theintermediate computer 8152 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server8148. Communication to the remote server 8148 may be streaming, batch(e.g., when a connection is available), or opportunistic.

The monitoring application 8150 may select subsets of the detectionvalues to be jointly analyzed. Subsets for analysis may be selectedbased on a single type of sensor, component, or a single type ofequipment in which a component is operating. Subsets for analysis may beselected or grouped based on common operating conditions such as size ofload, operational condition (e.g., intermittent or continuous),operating speed or tachometer output, common ambient environmentalconditions such as humidity, temperature, air or fluid particulate, andthe like. Subsets for analysis may be selected based on the effects ofother nearby equipment such as nearby machines rotating at similarfrequencies, nearby equipment producing electromagnetic fields, nearbyequipment producing heat, nearby equipment inducing movement orvibration, nearby equipment emitting vapors, chemicals or particulates,or other potentially interfering or intervening effects.

In embodiments, the monitoring application 8150 may analyze the selectedsubset. In an example, data from a single sensor may be analyzed overdifferent time periods such as one operating cycle, several operatingcycles, a month, a year, the life of the component, or the like. Datafrom multiple sensors of a common type measuring a common component typemay also be analyzed over different time periods. Trends in the datasuch as changing rates of change associated with start-up or differentpoints in the process may be identified. Correlation of trends andvalues for different sensors may be analyzed to identify thoseparameters whose short-term analysis might provide the best predictionregarding expected sensor performance. This information may betransmitted back to the monitoring device to update sensor models,sensor selection, sensor range, sensor scaling, sensor samplingfrequency, types of data collected, and the like, and be analyzedlocally or to influence the design of future monitoring devices.

In embodiments, the monitoring application 8150 may have access toequipment specifications, equipment geometry, component specifications,component materials, anticipated state information for a plurality ofsensors, operational history, historical detection values, sensor lifemodels, and the like for use analyzing the selected subset usingrule-based or model-based analysis. The monitoring application 8150 mayprovide recommendations regarding sensor selection, additional data tocollect, data to store with sensor data, and the like. The monitoringapplication 8150 may provide recommendations regarding schedulingrepairs and/or maintenance. The monitoring application 8150 may providerecommendations regarding replacing a sensor. The replacement sensor maymatch the sensor being replaced or the replacement sensor may have adifferent range, sensitivity, sampling frequency, and the like.

In embodiments, the monitoring application 8150 may include a remotelearning circuit structured to analyze sensor status data (e.g., sensoroverload or sensor failure) together with data from other sensors,failure data on components being monitored, equipment being monitored,output being produced, and the like. The remote learning system mayidentify correlations between sensor overload and data from othersensors.

An example monitoring system for data collection in an industrialenvironment includes a data acquisition circuit that interprets a numberof detection values, each of the detection values corresponding to inputreceived from at least one of a number of input sensors, a MUX havinginputs corresponding to a subset of the detection values, a MUX controlcircuit that interprets a subset of the number of detection values andprovides the logical control of the MUX and the correspondence of MUXinput and detected values as a result, where the logic control of theMUX includes adaptive scheduling of the select lines, a data analysiscircuit that receives an output from the MUX and data corresponding tothe logic control of the MUX resulting in a component health status, ananalysis response circuit that performs an operation in response to thecomponent health status, where the number of sensors includes at leasttwo sensors such as a temperature sensor, a load sensor, a vibrationsensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor,an accelerometer, a tri-axial vibration sensor, and/or a tachometer. Incertain further embodiments, an example system includes: where at leastone of the number of detection values may correspond to a fusion of twoor more input sensors representing a virtual sensor; where the systemfurther includes a data storage circuit that stores at least one ofcomponent specifications and anticipated component state information andbuffers a subset of the number of detection values for a predeterminedlength of time; where the system further includes a data storage circuitthat stores at least one of a component specification and anticipatedcomponent state information and buffers the output of the MUX and datacorresponding to the logic control of the MUX for a predetermined lengthof time; where the data analysis circuit includes a peak detectioncircuit, a phase detection circuit, a bandpass filter circuit, afrequency transformation circuit, a frequency analysis circuit, a PLLcircuit, a torsional analysis circuit, and/or a bearing analysiscircuit; where operation further includes storing additional data in thedata storage circuit; where the operation includes at least one ofenabling or disabling one or more portions of the MUX circuit; and/orwhere the operation includes causing the MUX control circuit to alterthe logical control of the MUX and the correspondence of MUX input anddetected values. In certain embodiments, the system includes at leasttwo multiplexers; control of the correspondence of the multiplexer inputand the detected values further includes controlling the connection ofthe output of a first multiplexer to an input of a second multiplexer;control of the correspondence of the multiplexer input and the detectedvalues further comprises powering down at least a portion of one of theat least two multiplexers; and/or control of the correspondence of MUXinput and detected values includes adaptive scheduling of the selectlines. In certain embodiments, a data response circuit analyzes thestream of data from one or both MUXes and recommends an action inresponse to the analysis.

An example testing system includes the testing system in communicationwith a number of analog and digital input sensors, a monitoring deviceincluding a data acquisition circuit that interprets a number ofdetection values, each of the number of detection values correspondingto at least one of the input sensors, a MUX having inputs correspondingto a subset of the detection values, a MUX control circuit thatinterprets a subset of the number of detection values and provides thelogical control of the MUX and control of the correspondence of MUXinput and detected values as a result, where the logic control of theMUX includes adaptive scheduling of the select lines, and a userinterface enabled to accept scheduling input for select lines anddisplay output of MUX and select line data.

In embodiments, information about the health or other status or stateinformation of or regarding a component or piece of industrial equipmentmay be obtained by looking at both the amplitude and phase or timing ofdata signals relative to related data signals, timers, reference signalsor data measurements. An embodiment of the data monitoring device 8500is shown in FIG. 59 and may include a plurality of sensors 8506communicatively coupled to a controller 8502. The controller 8502 mayinclude a data acquisition circuit 8504, a signal evaluation circuit8508 and a response circuit 8510. The plurality of sensors 8506 may bewired to ports on the data acquisition circuit 8504 or wirelessly incommunication with the data acquisition circuit 8504. The plurality ofsensors 8506 may be wirelessly connected to the data acquisition circuit8504. The data acquisition circuit 8504 may be able to access detectionvalues corresponding to the output of at least one of the plurality ofsensors 8506 where the sensors 8506 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.

The selection of the plurality of sensors 8506 for the data monitoringdevice 8500 designed for a specific component or piece of equipment maydepend on a variety of considerations such as accessibility forinstalling new sensors, incorporation of sensors in the initial design,anticipated operational and failure conditions, reliability of thesensors, and the like. The impact of failure may drive the extent towhich a component or piece of equipment is monitored with more sensorsand/or higher capability sensors being dedicated to systems whereunexpected or undetected failure would be costly or have severeconsequences.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, sensors8506 may comprise one or more of, without limitation, a vibrationsensor, a thermometer, a hygrometer, a voltage sensor, a current sensor,an accelerometer, a velocity detector, a light or electromagnetic sensor(e.g., determining temperature, composition and/or spectral analysis,and/or object position or movement), an image sensor, a structured lightsensor, a laser-based image sensor, an acoustic wave sensor, adisplacement sensor, a turbidity meter, a viscosity meter, a loadsensor, a tri-axial sensor, an accelerometer, a tachometer, a fluidpressure meter, an air flow meter, a horsepower meter, a flow ratemeter, a fluid particle detector, an acoustical sensor, a pH sensor, andthe like, including, without limitation, any of the sensors describedthroughout this disclosure and the documents incorporated by reference.

The sensors 8506 may provide a stream of data over time that has a phasecomponent, such as relating to acceleration or vibration, allowing forthe evaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors8506 may provide a stream of data that is not conventionallyphase-based, such as temperature, humidity, load, and the like. Thesensors 8506 may provide a continuous or near continuous stream of dataover time, periodic readings, event-driven readings, and/or readingsaccording to a selected interval or schedule.

In embodiments, as illustrated in FIG. 59, the sensors 8506 may be partof the data monitoring device 8500, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 60 and 61,sensors 8518, either new or previously attached to or integrated intothe equipment or component, may be opportunistically connected to oraccessed by a monitoring device 8512. The sensors 8518 may be directlyconnected to input ports 8520 on a data acquisition circuit 8516 of acontroller 8514 or may be accessed by the data acquisition circuit 8516wirelessly, such as by a reader, interrogator, or other wirelessconnection, such as over a short-distance wireless protocol. Inembodiments, the data acquisition circuit 8516 may access detectionvalues corresponding to the sensors 8518 wirelessly or via a separatesource or some combination of these methods. In embodiments, the dataacquisition circuit 8504 may include a wireless communications circuit8522 able to wirelessly receive data opportunistically from the sensors8518 in the vicinity and route the data to the input ports 8520 on thedata acquisition circuit 8516.

In an embodiment, as illustrated in FIGS. 62 and 63, the signalevaluation circuit 8508 may then process the detection values to obtaininformation about the component or piece of equipment being monitored.Information extracted by the signal evaluation circuit 8508 may compriserotational speed, vibrational data including amplitudes, frequencies,phase, and/or acoustical data, and/or non-phase sensor data such astemperature, humidity, image data, and the like.

The signal evaluation circuit 8508 may include one or more componentssuch as a phase detection circuit 8528 to determine a phase differencebetween two time-based signals, a phase lock loop circuit 8530 to adjustthe relative phase of a signal such that it is aligned with a secondsignal, timer or reference signal, and/or a band pass filter circuit8532 which may be used to separate out signals occurring at differentfrequencies. An example band pass filter circuit 8532 includes anyfiltering operations understood in the art, including at least alow-pass filter, a high-pass filter, and/or a band pass filter—forexample to exclude or reduce frequencies that are not of interest for aparticular determination, and/or to enhance the signal for frequenciesof interest. Additionally, or alternatively, the band pass filtercircuit 8532 includes one or more notch filters or other filteringmechanism to narrow ranges of frequencies (e.g., frequencies from aknown source of noise). This may be used to filter out dominantfrequency signals such as the overall rotation and may help enable theevaluation of low amplitude signals at frequencies associated withtorsion, bearing failure and the like.

In embodiments, understanding the relative differences may be enabled bya phase detection circuit 8528 to determine a phase difference betweentwo signals. It may be of value to understand a relative phase offset,if any, between signals such as when a periodic vibration occursrelative to a relative rotation of a piece of equipment. In embodiments,there may be value in understanding where in a cycle shaft vibrationsoccur relative to a motor control input to better balance the control ofthe motor. This may be particularly true for systems and components thatare operating at relative slow RPMs. Understanding of the phasedifference between two signals or between those signals and a timer mayenable establishing a relationship between a signal value and where itoccurs in a process or rotation. Understanding relative phasedifferences may help in evaluating the relationship between differentcomponents of a system such as in the creation of a vibrational modelfor an Operational Deflection Shape (ODS).

The signal evaluation circuit 8544 may perform frequency analysis usingtechniques such as a digital Fast Fourier transform (FFT), Laplacetransform, Z-transform, wavelet transform, other frequency domaintransform, or other digital or analog signal analysis techniques,including, without limitation, complex analysis, including complex phaseevolution analysis. An overall rotational speed or tachometer may bederived from data from sensors such as rotational velocity meters,accelerometers, displacement meters and the like. Additional frequenciesof interest may also be identified. These may include frequencies nearthe overall rotational speed as well as frequencies higher than that ofthe rotational speed. These may include frequencies that arenonsynchronous with an overall rotational speed. Signals observed atfrequencies that are multiples of the rotational speed may be due tobearing induced vibrations or other behaviors or situations involvingbearings. In some instances, these frequencies may be in the range ofone times the rotational speed, two times the rotational speed, threetimes the rotational speed, and the like, up to 3.15 to 15 times therotational speed, or higher. In some embodiments, the signal evaluationcircuit 8544 may select RC components for the band pass filter circuit8532 based on overall rotational speed to create the band pass filtercircuit 8532 to remove signals at expected frequencies such as theoverall rotational speed, to facilitate identification of smallamplitude signals at other frequencies. In embodiments, variablecomponents may be selected, such that adjustments may be made in keepingwith changes in the rotational speed, so that the band pass filter maybe a variable band pass filter. This may occur under control ofautomatically self-adjusting circuit elements, or under control of aprocessor, including automated control based on a model of the circuitbehavior, where a rotational speed indicator or other data is providedas a basis for control.

In embodiments, rather than performing frequency analysis, the signalevaluation circuit 8544 may utilize the time-based detection values toperform transitory signal analysis. These may include identifying abruptchanges in signal amplitude including changes where the change inamplitude exceeds a predetermined value or exists for a certainduration. In embodiments, the time-based sensor data may be aligned witha timer or reference signal allowing the time-based sensor data to bealigned with, for example, a time or location in a cycle. Additionalprocessing to look at frequency changes over time may include the use ofShort-Time Fourier Transforms (STFT) or a wavelet transform.

In embodiments, frequency-based techniques and time-based techniques maybe combined, such as using time-based techniques to determine discretetime periods during which given operational modes or states areoccurring and using frequency-based techniques to determine behaviorwithin one or more of the discrete time periods.

In embodiments, the signal evaluation circuit may utilize demodulationtechniques for signals obtained from equipment running at slow speedssuch as paper and pulp machines, mining equipment, and the like. Asignal evaluation circuit employing a demodulation technique maycomprise a band-pass filter circuit, a rectifier circuit, and/or a lowpass circuit prior to transforming the data to the frequency domain.

The response circuit 8510 8710 may further comprise evaluating theresults of the signal evaluation circuit 8508 8544 and, based on certaincriteria, initiating an action. Criteria may include a predeterminedmaximum or minimum value for a detection value from a specific sensor, avalue of a sensor's corresponding detection value over time, a change invalue, a rate of change in value, and/or an accumulated value (e.g., atime spent above/below a threshold value, a weighted time spentabove/below one or more threshold values, and/or an area of the detectedvalue above/below one or more threshold values). The criteria mayinclude a sensor's detection values at certain frequencies or phaseswhere the frequencies or phases may be based on the equipment geometry,equipment control schemes, system input, historical data, currentoperating conditions, and/or an anticipated response. The criteria maycomprise combinations of data from different sensors such as relativevalues, relative changes in value, relative rates of change in value,relative values over time, and the like. The relative criteria maychange with other data or information such as process stage, type ofproduct being processed, type of equipment, ambient temperature andhumidity, external vibrations from other equipment, and the like. Therelative criteria may include level of synchronicity with an overallrotational speed, such as to differentiate between vibration induced bybearings and vibrations resulting from the equipment design. Inembodiments, the criteria may be reflected in one or more calculatedstatistics or metrics (including ones generated by further calculationson multiple criteria or statistics), which in turn may be used forprocessing (such as on board a data collector or by an external system),such as to be provided as an input to one or more of the machinelearning capabilities described in this disclosure, to a control system(which may be an on-board data collector or remote, such as to controlselection of data inputs, multiplexing of sensor data, storage, or thelike), or as a data element that is an input to another system, such asa data stream or data package that may be available to a datamarketplace, a SCADA system, a remote control system, a maintenancesystem, an analytic system, or other system.

In an illustrative and non-limiting example, an alert may be issued ifthe vibrational amplitude and/or frequency exceeds a predeterminedmaximum value, if there is a change or rate of change that exceeds apredetermined acceptable range, and/or if an accumulated value based onvibrational amplitude and/or frequency exceeds a threshold. Certainembodiments are described herein as detected values exceeding thresholdsor predetermined values but detected values may also fall belowthresholds or predetermined values—for example where an amount of changein the detected value is expected to occur, but detected values indicatethat the change may not have occurred. For example, and withoutlimitation, vibrational data may indicate system agitation levels,properly operating equipment, or the like, and vibrational data belowamplitude and/or frequency thresholds may be an indication of a processthat is not operating according to expectations. Except where thecontext clearly indicates otherwise, any description herein describing adetermination of a value above a threshold and/or exceeding apredetermined or expected value is understood to include determinationof a value below a threshold and/or falling below a predetermined orexpected value.

The predetermined acceptable range may be based on anticipated systemresponse or vibration based on the equipment geometry and control schemesuch as number of bearings, relative rotational speed, influx of powerto the system at a certain frequency, and the like. The predeterminedacceptable range may also be based on long term analysis of detectionvalues across a plurality of similar equipment and components andcorrelation of data with equipment failure. Based on vibration phaseinformation, a physical location of a problem may be identified. Basedon the vibration phase information system design flaws, off-nominaloperation, and/or component or process failures may be identified. Insome embodiments, an alert may be issued based on changes or rates ofchange in the data over time such as increasing amplitude or shifts inthe frequencies or phases at which a vibration occurs. In someembodiments, an alert may be issued based on accumulated values such astime spent over a threshold, weighted time spent over one or morethresholds, and/or an area of a curve of the detected value over one ormore thresholds. In embodiments, an alert may be issued based on acombination of data from different sensors such as relative changes invalue, or relative rates of change in amplitude, frequency of phase inaddition to values of non-phase sensors such as temperature, humidityand the like. For example, an increase in temperature and energy atcertain frequencies may indicate a hot bearing that is starting to fail.In embodiments, the relative criteria for an alarm may change with otherdata or information such as process stage, type of product beingprocessed on equipment, ambient temperature and humidity, externalvibrations from other equipment and the like.

In embodiments, the response circuit 8510 may cause the data acquisitioncircuit 8504 to enable or disable the processing of detection valuescorresponding to certain sensors based on the some of the criteriadiscussed above. This may include switching to sensors having differentresponse rates, sensitivity, ranges, and the like; accessing new sensorsor types of sensors, and the like. Switching may be undertaken based ona model, a set of rules, or the like. In embodiments, switching may beunder control of a machine learning system, such that switching iscontrolled based on one or more metrics of success, combined with inputdata, over a set of trials, which may occur under supervision of a humansupervisor or under control of an automated system. Switching mayinvolve switching from one input port to another (such as to switch fromone sensor to another). Switching may involve altering the multiplexingof data, such as combining different streams under differentcircumstances. Switching may involve activating a system to obtainadditional data, such as moving a mobile system (such as a robotic ordrone system), to a location where different or additional data isavailable (such as positioning an image sensor for a different view orpositioning a sonar sensor for a different direction of collection) orto a location where different sensors can be accessed (such as moving acollector to connect up to a sensor that is disposed at a location in anenvironment by a wired or wireless connection). The response circuit8510 may make recommendations for the replacement of certain sensors inthe future with sensors having different response rates, sensitivity,ranges, and the like. The response circuit 8510 may recommend designalterations for future embodiments of the component, the piece ofequipment, the operating conditions, the process, and the like.

In embodiments, the response circuit 8510 may recommend maintenance atan upcoming process stop or initiate a maintenance call. The responsecircuit 8510 may recommend changes in process or operating parameters toremotely balance the piece of equipment. In embodiments, the responsecircuit 8510 may implement or recommend process changes—for example tolower the utilization of a component that is near a maintenanceinterval, operating off-nominally, or failed for purpose but still atleast partially operational, to change the operating speed of acomponent (such as to put it in a lower-demand mode), to initiateamelioration of an issue (such as to signal for additional lubricationof a roller bearing set, or to signal for an alignment process for asystem that is out of balance), and the like.

In embodiments, as shown in FIG. 64, a data monitoring device 8540 mayfurther comprise a data storage circuit 8542, memory, and the like. Thesignal evaluation circuit 8544 may periodically store certain detectionvalues to enable the tracking of component performance over time.

In embodiments, based on relevant operating conditions and/or failuremodes which may occur in as sensor values approach one or more criteria,the signal evaluation circuit 8544 may store data in the data storagecircuit 8542 based on the fit of data relative to one or more criteria,such as those described throughout this disclosure. Based on one sensorinput meeting or approaching specified criteria or range, the signalevaluation circuit 8544 may store additional data such as RPMs,component loads, temperatures, pressures, vibrations or other sensordata of the types described throughout this disclosure. The signalevaluation circuit 8544 may store data at a higher data rate for greatergranularity in future processing, the ability to reprocess at differentsampling rates, and/or to enable diagnosing or post-processing of systeminformation where operational data of interest is flagged, and the like.

In embodiments, as shown in FIG. 65, a data monitoring system 8546 maycomprise at least one data monitoring device 8548. The at least one datamonitoring device 8548 comprising sensors 8506, a controller 8550comprising a data acquisition circuit 8504, a signal evaluation circuit8538, a data storage circuit 8542, and a communications circuit 8552 toallow data and analysis to be transmitted to a monitoring application8556 on a remote server 8554. The signal evaluation circuit 8538 maycomprise at least one of a phase detection circuit 8528, a phase lockloop circuit 8530, and/or a band pass circuit 8532. The signalevaluation circuit 8538 may periodically share data with thecommunication circuit 8552 for transmittal to the remote server 8554 toenable the tracking of component and equipment performance over time andunder varying conditions by the monitoring application 8556. Becauserelevant operating conditions and/or failure modes may occur as sensorvalues approach one or more criteria, the signal evaluation circuit 8538may share data with the communication circuit 8552 for transmittal tothe remote server 8554 based on the fit of data relative to one or morecriteria. Based on one sensor input meeting or approaching specifiedcriteria or range, the signal evaluation circuit 8538 may shareadditional data such as RPMs, component loads, temperatures, pressures,vibrations, and the like for transmittal. The signal evaluation circuit8538 may share data at a higher data rate for transmittal to enablegreater granularity in processing on the remote server.

In embodiments, as illustrated in FIG. 66, a data collection system 8560may have a plurality of monitoring devices 8558 collecting data onmultiple components in a single piece of equipment, collecting data onthe same component across a plurality of pieces of equipment (both thesame and different types of equipment) in the same facility, as well ascollecting data from monitoring devices in multiple facilities. Amonitoring application on a remote server may receive and store the datacoming from a plurality of the various monitoring devices. Themonitoring application may then select subsets of data which may bejointly analyzed. Subsets of monitoring data may be selected based ondata from a single type of component or data from a single type ofequipment in which the component is operating. Monitoring data may beselected or grouped based on common operating conditions such as size ofload, operational condition (e.g., intermittent, continuous), operatingspeed or tachometer, common ambient environmental conditions such ashumidity, temperature, air or fluid particulate, and the like.Monitoring data may be selected based on the effects of other nearbyequipment, such as nearby machines rotating at similar frequencies,nearby equipment producing electromagnetic fields, nearby equipmentproducing heat, nearby equipment inducing movement or vibration, nearbyequipment emitting vapors, chemicals or particulates, or otherpotentially interfering or intervening effects.

The monitoring application may then analyze the selected data set. Forexample, data from a single component may be analyzed over differenttime periods such as one operating cycle, several operating cycles, amonth, a year, or the like. Data from multiple components of the sametype may also be analyzed over different time periods. Trends in thedata such as changes in frequency or amplitude may be correlated withfailure and maintenance records associated with the same component orpiece of equipment. Trends in the data such as changing rates of changeassociated with start-up or different points in the process may beidentified. Additional data may be introduced into the analysis such asoutput product quality, output quantity (such as per unit of time),indicated success or failure of a process, and the like. Correlation oftrends and values for different types of data may be analyzed toidentify those parameters whose short-term analysis might provide thebest prediction regarding expected performance. This information may betransmitted back to the monitoring device to update types of datacollected and analyzed locally or to influence the design of futuremonitoring devices.

In an illustrative and non-limiting example, the monitoring device maybe used to collect and process sensor data to measure mechanical torque.The monitoring device may be in communication with or include a highresolution, high speed vibration sensor to collect data over an extendedperiod of time, enough to measure multiple cycles of rotation. For geardriven equipment, the sampling resolution should be such that the numberof samples taken per cycle is at least equal to the number of gear teethdriving the component. It will be understood that a lower samplingresolution may also be utilized, which may result in a lower confidencedetermination and/or taking data over a longer period of time to developsufficient statistical confidence. This data may then be used in thegeneration of a phase reference (relative probe) or tachometer signalfor a piece of equipment. This phase reference may be used to alignphase data such as vibrational data or acceleration data from multiplesensors located at different positions on a component or on differentcomponents within a system. This information may facilitate thedetermination of torque for different components or the generation of anOperational Deflection Shape (ODS), indicating the extent of mechanicaldeflection of one or more components during an operational mode, whichin turn may be used to measure mechanical torque in the component.

The higher resolution data stream may provide additional data for thedetection of transitory signals in low speed operations. Theidentification of transitory signals may enable the identification ofdefects in a piece of equipment or component

In an illustrative and non-limiting example, the monitoring device maybe used to identify mechanical jitter for use in failure predictionmodels. The monitoring device may begin acquiring data when the piece ofequipment starts up through ramping up to operating speed and thenduring operation. Once at operating speed, it is anticipated that thetorsional jitter should be minimal and changes in torsion during thisphase may be indicative of cracks, bearing faults and the like.Additionally, known torsions may be removed from the signal tofacilitate in the identification of unanticipated torsions resultingfrom system design flaws or component wear. Having phase informationassociated with the data collected at operating speed may facilitateidentification of a location of vibration and potential component wear.Relative phase information for a plurality of sensors located throughouta machine may facilitate the evaluation of torsion as it is propagatedthrough a piece of equipment.

An example system data collection in an industrial environment includesa data acquisition circuit that interprets a number of detection valuesfrom a number of input sensors communicatively coupled to the dataacquisition circuit, each of the number of detection valuescorresponding to at least one of the input sensors, a signal evaluationcircuit that obtains at least one of a vibration amplitude, a vibrationfrequency and a vibration phase location corresponding to at least oneof the input sensors in response to the number of detection values, anda response circuit that performs at least one operation in response toat the at least one of the vibration amplitude, the vibration frequencyand the vibration phase location. Certain further embodiments of anexample system include: where the signal evaluation circuit includes aphase detection circuit, or a phase detection circuit and a phase lockloop circuit and/or a band pass filter; where the number of inputsensors includes at least two input sensors providing phase informationand at least one input sensor providing non-phase sensor information;the signal evaluation circuit further aligning the phase informationprovided by the at least two of the input sensors; where the at leastone operation is further in response to at least one of: a change inmagnitude of the vibration amplitude; a change in frequency or phase ofvibration; a rate of change in at least one of vibration amplitude,vibration frequency and vibration phase; a relative change in valuebetween at least two of vibration amplitude, vibration frequency andvibration phase; and/or a relative rate of change between at least twoof vibration amplitude, vibration frequency, and vibration phase; thesystem further including an alert circuit, where the at least oneoperation includes providing an alert and where the alert may be one ofhaptic, audible and visual; a data storage circuit, where at least oneof the vibration amplitude, vibration frequency, and vibration phase isstored periodically to create a vibration history, and where the atleast one operation includes storing additional data in the data storagecircuit (e.g., as a vibration fingerprint for a component); where thestoring additional data in the data storage circuit is further inresponse to at least one of: a change in magnitude of the vibrationamplitude; a change in frequency or phase of vibration; a rate of changein the vibration amplitude, frequency or phase; a relative change invalue between at least two of vibration amplitude, frequency and phase;and a relative rate of change between at least two of vibrationamplitude, frequency and phase; the system further comprising at leastone of a multiplexing (MUX) circuit whereby alternative combinations ofdetection values may be selected based on at least one of user input, adetected state, and a selected operating parameter for a machine; whereeach of the number of detection values corresponds to at least one ofthe input sensors; where the at least one operation includes enabling ordisabling the connection of one or more portions of the multiplexingcircuit; a MUX control circuit that interprets a subset of the number ofdetection values and provides the logical control of the MUX and thecorrespondence of MUX input and detected values as a result; and/orwhere the logic control of the MUX includes adaptive scheduling of theselect lines.

An example method of monitoring a component, includes receivingtime-based data from at least one sensor, phase-locking the receiveddata with a reference signal, transforming the received time-based datato frequency data, filtering the frequency data to remove tachometerfrequencies, identifying low amplitude signals occurring at highfrequencies, and activating an alarm if a low amplitude signal exceeds athreshold.

An example system for data collection, processing, and utilization ofsignals in an industrial environment includes a plurality of monitoringdevices, each monitoring device comprising a data acquisition circuitstructured to interpret a plurality of detection values from a pluralityof input sensors communicatively coupled to the data acquisitioncircuit, each of the plurality of detection values corresponding to atleast one of the input sensors; a signal evaluation circuit structuredto obtain at least one of vibration amplitude, vibration frequency and avibration phase location corresponding to at least one of the inputsensors in response to the corresponding at least one of the pluralityof detection values; a data storage facility for storing a subset of theplurality of detection values; a communication circuit structured tocommunicate at least one selected detection value to a remote server;and a monitoring application on the remote server structured to: receivethe at least one selected detection value; jointly analyze a subset ofthe detection values received from the plurality of monitoring devices;and recommend an action.

In certain further embodiments, an example system includes: for eachmonitoring device, the plurality of input sensors include at least oneinput sensor providing phase information and at least one input sensorproviding non-phase input sensor information and where joint analysisincludes using the phase information from the plurality of monitoringdevices to align the information from the plurality of monitoringdevices; where the subset of detection values is selected based on dataassociated with a detection value including at least one: common type ofcomponent, common type of equipment, and common operating conditions andfurther selected based on one of anticipated life of a componentassociated with detection values, type of the equipment associated withdetection values, and operational conditions under which detectionvalues were measured; and/or where the analysis of the subset ofdetection values includes feeding a neural net with the subset ofdetection values and supplemental information to learn to recognizevarious operating states, health states, life expectancies and faultstates utilizing deep learning techniques. In embodiments, thesupplemental information comprises one of component specification,component performance, equipment specification, equipment performance,maintenance records, repair records and an anticipated state model.

An example system for data collection in an industrial environmentincludes a data acquisition circuit structured to interpret a pluralityof detection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; asignal evaluation circuit structured to obtain at least one of vibrationamplitude, vibration frequency and vibration phase locationcorresponding to at least one of the input sensors in response to thecorresponding at least one of a plurality of detection values; amultiplexing circuit whereby alternative combinations of the detectionvalues may be selected based on at least one of user input, a detectedstate and a selected operating parameter for a machine, each of theplurality of detection values corresponding to at least one of the inputsensors; and a response circuit structured to perform at least oneoperation in response to at the at least one of the vibration amplitude,vibration frequency and vibration phase location.

An example system for data collection in a piece of equipment, includesa data acquisition circuit structured to interpret a plurality ofdetection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; atimer circuit structured to generate a timing signal based on a firstdetected value of the plurality of detection values; a signal evaluationcircuit structured to obtain at least one of vibration amplitude,vibration frequency and vibration phase location corresponding to asecond detected value comprising: a phase detection circuit structuredto determine a relative phase difference between a second detectionvalue of the plurality of detection values and the timing signal; and aresponse circuit structured to perform at least one operation inresponse to at the at least one of the vibration amplitude, vibrationfrequency and vibration phase location.

An example system for bearing analysis in an industrial environment,includes a data acquisition circuit structured to interpret a pluralityof detection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage for storing specifications and anticipated stateinformation for a plurality of bearing types and buffering the pluralityof detection values for a predetermined length of time; a timer circuitstructured to generate a timing signal based on a first detected valueof the plurality of detection values; a bearing analysis circuitstructured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a lifeprediction comprising: a phase detection circuit structured to determinea relative phase difference between a second detection value of theplurality of detection values and the timing signal; and a signalevaluation circuit structured to obtain at least one of vibrationamplitude, vibration frequency and vibration phase locationcorresponding to a second detected value: and a response circuitstructured to perform at least one operation in response to at the atleast one of the vibration amplitude, vibration frequency and vibrationphase location.

An example motor monitoring system includes: a data acquisition circuitstructured to interpret a plurality of detection values from a pluralityof input sensors communicatively coupled to the data acquisitioncircuit, each of the plurality of detection values corresponding to atleast one of the input sensors; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor the motor and motor components, store historical motor performanceand buffer the plurality of detection values for a predetermined lengthof time; a timer circuit structured to generate a timing signal based ona first detected value of the plurality of detection values; a motoranalysis circuit structured to analyze buffered detection valuesrelative to specifications and anticipated state information resultingin a motor performance parameter comprising: a phase detection circuitstructured to determine a relative phase difference between a seconddetection value of the plurality of detection values and the timingsignal; and a signal evaluation circuit structured to obtain at leastone of vibration amplitude, vibration frequency and vibration phaselocation corresponding to a second detected value and analyze the atleast one of vibration amplitude, vibration frequency and vibrationphase location relative to buffered detection values, specifications andanticipated state information resulting in a motor performanceparameter; and a response circuit structured to perform at least oneoperation in response to at the at least one of vibration amplitude,vibration frequency and vibration phase location and motor performanceparameter.

An example system for estimating a vehicle steering system performanceparameter, includes: a data acquisition circuit structured to interpreta plurality of detection values from a plurality of input sensorscommunicatively coupled to the data acquisition circuit, each of theplurality of detection values corresponding to at least one of the inputsensors; a data storage circuit structured to store specifications,system geometry, and anticipated state information for the vehiclesteering system, the rack, the pinion, and the steering column, storehistorical steering system performance and buffer the plurality ofdetection values for a predetermined length of time; a timer circuitstructured to generate a timing signal based on a first detected valueof the plurality of detection values; a steering system analysis circuitstructured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a steeringsystem performance parameter comprising: a phase detection circuitstructured to determine a relative phase difference between a seconddetection value of the plurality of detection values and the timingsignal; and a signal evaluation circuit structured to obtain at leastone of vibration amplitude, vibration frequency and vibration phaselocation corresponding to a second detected value and analyze the atleast one of vibration amplitude, vibration frequency and vibrationphase location relative to buffered detection values, specifications andanticipated state information resulting in a steering system performanceparameter; and a response circuit structured to perform at least oneoperation in response to at the at least one of vibration amplitude,vibration frequency and vibration phase location and the steering systemperformance parameter.

An example system for estimating a health parameter a pump performanceparameter includes a data acquisition circuit structured to interpret aplurality of detection values from a plurality of input sensorscommunicatively coupled to the data acquisition circuit, each of theplurality of detection values corresponding to at least one of the inputsensors; a data storage circuit structured to store specifications,system geometry, and anticipated state information for the pump and pumpcomponents associated with the detection values, store historical pumpperformance and buffer the plurality of detection values for apredetermined length of time; a timer circuit structured to generate atiming signal based on a first detected value of the plurality ofdetection values; a pump analysis circuit structured to analyze buffereddetection values relative to specifications and anticipated stateinformation resulting in a pump performance parameter comprising: aphase detection circuit structured to determine a relative phasedifference between a second detection value of the plurality ofdetection values and the timing signal; and a signal evaluation circuitstructured to obtain at least one of vibration amplitude, vibrationfrequency and vibration phase location corresponding to a seconddetected value and analyze the at least one of vibration amplitude,vibration frequency and vibration phase location relative to buffereddetection values, specifications and anticipated state informationresulting in a pump performance parameter; and a response circuitstructured to perform at least one operation in response to at the atleast one of vibration amplitude, vibration frequency and vibrationphase location and the pump performance parameter. In embodiments, thepump is one of a water pump in a car and a mineral pump.

An example system for estimating a drill performance parameter for adrilling machine, includes: a data acquisition circuit structured tointerpret a plurality of detection values from a plurality of inputsensors communicatively coupled to the data acquisition circuit, each ofthe plurality of detection values corresponding to at least one of theinput sensors; a data storage circuit structured to storespecifications, system geometry, and anticipated state information forthe drill and drill components associated with the detection values,store historical drill performance and buffer the plurality of detectionvalues for a predetermined length of time; a timer circuit structured togenerate a timing signal based on a first detected value of theplurality of detection values; a drill analysis circuit structured toanalyze buffered detection values relative to specifications andanticipated state information resulting in a drill performance parametercomprising: a phase detection circuit structured to determine a relativephase difference between a second detection value of the plurality ofdetection values and the timing signal; and a signal evaluation circuitstructured to obtain at least one of vibration amplitude, vibrationfrequency and vibration phase location corresponding to a seconddetected value and analyze the at least one of vibration amplitude,vibration frequency and vibration phase location relative to buffereddetection values, specifications and anticipated state informationresulting in a drill performance parameter; and a response circuitstructured to perform at least one operation in response to at the atleast one of vibration amplitude, vibration frequency and vibrationphase location and the drill performance parameter. In embodiments, thedrilling machine is one of an oil drilling machine and a gas drillingmachine.

An example system for estimating a conveyor health parameter, includes:a data acquisition circuit structured to interpret a plurality ofdetection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for a conveyor and conveyorcomponents associated with the detection values, store historicalconveyor performance and buffer the plurality of detection values for apredetermined length of time; a timer circuit structured to generate atiming signal based on a first detected value of the plurality ofdetection values; a conveyor analysis circuit structured to analyzebuffered detection values relative to specifications and anticipatedstate information resulting in a conveyor performance parametercomprising: a phase detection circuit structured to determine a relativephase difference between a second detection value of the plurality ofdetection values and the timing signal; and a signal evaluation circuitstructured to obtain at least one of vibration amplitude, vibrationfrequency and vibration phase location corresponding to a seconddetected value and analyze the at least one of vibration amplitude,vibration frequency and vibration phase location relative to buffereddetection values, specifications and anticipated state informationresulting in a conveyor performance parameter; and a response circuitstructured to perform at least one operation in response to at the atleast one of vibration amplitude, vibration frequency and vibrationphase location and the conveyor performance parameter.

An example system for estimating an agitator health parameter, includes:a data acquisition circuit structured to interpret a plurality ofdetection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for an agitator and agitatorcomponents associated with the detection values, store historicalagitator performance and buffer the plurality of detection values for apredetermined length of time; a timer circuit structured to generate atiming signal based on a first detected value of the plurality ofdetection values; an agitator analysis circuit structured to analyzebuffered detection values relative to specifications and anticipatedstate information resulting in an agitator performance parametercomprising: a phase detection circuit structured to determine a relativephase difference between a second detection value of the plurality ofdetection values and the timing signal; and a signal evaluation circuitstructured to obtain at least one of vibration amplitude, vibrationfrequency and vibration phase location corresponding to a seconddetected value and analyze the at least one of vibration amplitude,vibration frequency and vibration phase location relative to buffereddetection values, specifications and anticipated state informationresulting in an agitator performance parameter; and a response circuitstructured to perform at least one operation in response to at the atleast one of vibration amplitude, vibration frequency and vibrationphase location and the agitator performance parameter. In embodiments,the agitator is one of a rotating tank mixer, a large tank mixer, aportable tank mixers, a tote tank mixer, a drum mixer, a mounted mixerand a propeller mixer.

An example system for estimating a compressor health parameter,includes: a data acquisition circuit structured to interpret a pluralityof detection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for a compressor andcompressor components associated with the detection values, storehistorical compressor performance and buffer the plurality of detectionvalues for a predetermined length of time; a timer circuit structured togenerate a timing signal based on a first detected value of theplurality of detection values; a compressor analysis circuit structuredto analyze buffered detection values relative to specifications andanticipated state information resulting in a compressor performanceparameter comprising: a phase detection circuit structured to determinea relative phase difference between a second detection value of theplurality of detection values and the timing signal; and a signalevaluation circuit structured to obtain at least one of vibrationamplitude, vibration frequency and vibration phase locationcorresponding to a second detected value and analyze the at least one ofvibration amplitude, vibration frequency and vibration phase locationrelative to buffered detection values, specifications and anticipatedstate information resulting in a compressor performance parameter; and aresponse circuit structured to perform at least one operation inresponse to at the at least one of vibration amplitude, vibrationfrequency and vibration phase location and the compressor performanceparameter.

An example system for estimating an air conditioner health parameter,includes: a data acquisition circuit structured to interpret a pluralityof detection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for an air conditioner andair conditioner components associated with the detection values, storehistorical air conditioner performance and buffer the plurality ofdetection values for a predetermined length of time; a timer circuitstructured to generate a timing signal based on a first detected valueof the plurality of detection values; an air conditioner analysiscircuit structured to analyze buffered detection values relative tospecifications and anticipated state information resulting in an airconditioner performance parameter comprising: a phase detection circuitstructured to determine a relative phase difference between a seconddetection value of the plurality of detection values and the timingsignal; and a signal evaluation circuit structured to obtain at leastone of vibration amplitude, vibration frequency and vibration phaselocation corresponding to a second detected value and analyze the atleast one of vibration amplitude, vibration frequency and vibrationphase location relative to buffered detection values, specifications andanticipated state information resulting in an air conditionerperformance parameter; and a response circuit structured to perform atleast one operation in response to at the at least one of vibrationamplitude, vibration frequency and vibration phase location and the airconditioner performance parameter.

An example system for estimating a centrifuge health parameter,includes: a data acquisition circuit structured to interpret a pluralityof detection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for a centrifuge andcentrifuge components associated with the detection values, storehistorical centrifuge performance and buffer the plurality of detectionvalues for a predetermined length of time; a timer circuit structured togenerate a timing signal based on a first detected value of theplurality of detection values; a centrifuge analysis circuit structuredto analyze buffered detection values relative to specifications andanticipated state information resulting in a centrifuge performanceparameter comprising: a phase detection circuit structured to determinea relative phase difference between a second detection value of theplurality of detection values and the timing signal; and a signalevaluation circuit structured to obtain at least one of vibrationamplitude, vibration frequency and vibration phase locationcorresponding to a second detected value and analyze the at least one ofvibration amplitude, vibration frequency and vibration phase locationrelative to buffered detection values, specifications and anticipatedstate information resulting in a centrifuge performance parameter; and aresponse circuit structured to perform at least one operation inresponse to at the at least one of vibration amplitude, vibrationfrequency and vibration phase location and the centrifuge performanceparameter.

In embodiments, information about the health of a component or piece ofindustrial equipment may be obtained by comparing the values of multiplesignals at the same point in a process. This may be accomplished byaligning a signal relative to other related data signals, timers, orreference signals. An embodiment of a data monitoring device 8700, 8718is shown in FIGS. 67-69 and may include a controller 8702, 8720. Thecontroller may include a data acquisition circuit 8704, 8722, a signalevaluation circuit 8708, a data storage circuit 8716 and an optionalresponse circuit 8710. The signal evaluation circuit 8708 may comprise atimer circuit 8714 and, optionally, a phase detection circuit 8712.

The data monitoring device may include a plurality of sensors 8706communicatively coupled to the controller 8702. The plurality of sensors8706 may be wired to ports on the data acquisition circuit 8704. Theplurality of sensors 8706 may be wirelessly connected to the dataacquisition circuit 8704 which may be able to access detection valuescorresponding to the output of at least one of the plurality of sensors8706 where the sensors 8706 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.In embodiments, as illustrated in FIGS. 68 and 69, one or more externalsensors 8724 which are not explicitly part of a monitoring device 8718may be opportunistically connected to or accessed by the monitoringdevice 8718. The data acquisition circuit 8722 may include one or moreinput ports 8726. The one or more external sensors 8724 may be directlyconnected to the one or more input ports 8726 on the data acquisitioncircuit 8722 of the controller 8720. In embodiments, as shown in FIG.69, a data acquisition circuit 8722 may further comprise a wirelesscommunications circuit 8728 to access detection values corresponding tothe one or more external sensors 8724 wirelessly or via a separatesource or some combination of these methods.

The selection of the plurality of sensors 8706 8724 for connection tothe data monitoring device 8700 8718 designed for a specific componentor piece of equipment may depend on a variety of considerations such asaccessibility for installing new sensors, incorporation of sensors inthe initial design, anticipated operational and failure conditions,resolution desired at various positions in a process or plant,reliability of the sensors, and the like. The impact of a failure, timeresponse of a failure (e.g., warning time and/or off-nominal modesoccurring before failure), likelihood of failure, and/or sensitivityrequired and/or difficulty to detect failed conditions may drive theextent to which a component or piece of equipment is monitored with moresensors and/or higher capability sensors being dedicated to systemswhere unexpected or undetected failure would be costly or have severeconsequences.

The signal evaluation circuit 8708 may process the detection values toobtain information about a component or piece of equipment beingmonitored. Information extracted by the signal evaluation circuit 8708may comprise information regarding what point or time in a processcorresponds with a detection value where the point in time is based on atiming signal generated by the timer circuit 8714. The start of thetiming signal may be generated by detecting an edge of a control signalsuch as a rising edge, falling edge or both where the control signal maybe associated with the start of a process. The start of the timingsignal may be triggered by an initial movement of a component or pieceof equipment. The start of the timing signal may be triggered by aninitial flow through a pipe or opening or by a flow achieving apredetermined rate. The start of the timing signal may be triggered by astate value indicating a process has commenced—for example the state ofa switch, button, data value provided to indicate the process hascommenced, or the like. Information extracted may comprise informationregarding a difference in phase, determined by the phase detectioncircuit 8712, between a stream of detection value and the time signalgenerated by the timer circuit 8714. Information extracted may compriseinformation regarding a difference in phase between one stream ofdetection values and a second stream of detection values where the firststream of detection values is used as a basis or trigger for a timingsignal generated by the timer circuit.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, sensors8706 8724 may comprise one or more of, without limitation, athermometer, a hygrometer, a voltage sensor, a current sensor, anaccelerometer, a velocity detector, a light or electromagnetic sensor(e.g., determining temperature, composition and/or spectral analysis,and/or object position or movement), an image sensor, a displacementsensor, a turbidity meter, a viscosity meter, a load sensor, a tri-axialsensor, a tachometer, a fluid pressure meter, an air flow meter, ahorsepower meter, a flow rate meter, a fluid particle detector, anacoustical sensor, a pH sensor, and the like.

The sensors 8706 8724 may provide a stream of data over time that has aphase component, such as acceleration or vibration, allowing for theevaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors8706 8724 may provide a stream of data that is not phase based such astemperature, humidity, load, and the like. The sensors 8706 8724 mayprovide a continuous or near continuous stream of data over time,periodic readings, event-driven readings, and/or readings according to aselected interval or schedule.

In embodiments, as illustrated in FIGS. 70 and 71, the data acquisitioncircuit 8734 may further comprise a multiplexer circuit 8736 asdescribed elsewhere herein. Outputs from the multiplexer circuit 8736may be utilized by the signal evaluation circuit 8708. The responsecircuit 8710 may have the ability to turn on and off portions of themultiplexer circuit 8736. The response circuit 8710 may have the abilityto control the control channels of the multiplexer circuit 8736

The response circuit 8710 may further comprise evaluating the results ofthe signal evaluation circuit 8708 and, based on certain criteria,initiating an action. The criteria may include a sensor's detectionvalues at certain frequencies or phases relative to the timer signalwhere the frequencies or phases of interest may be based on theequipment geometry, equipment control schemes, system input, historicaldata, current operating conditions, and/or an anticipated response.Criteria may include a predetermined maximum or minimum value for adetection value from a specific sensor, a cumulative value of a sensor'scorresponding detection value over time, a change in value, a rate ofchange in value, and/or an accumulated value (e.g., a time spentabove/below a threshold value, a weighted time spent above/below one ormore threshold values, and/or an area of the detected value above/belowone or more threshold values). The criteria may comprise combinations ofdata from different sensors such as relative values, relative changes invalue, relative rates of change in value, relative values over time, andthe like. The relative criteria may change with other data orinformation such as process stage, type of product being processed, typeof equipment, ambient temperature and humidity, external vibrations fromother equipment, and the like.

Certain embodiments are described herein as detected values exceedingthresholds or predetermined values but detected values may also fallbelow thresholds or predetermined values—for example where an amount ofchange in the detected value is expected to occur, but detected valuesindicate that the change may not have occurred. For example, and withoutlimitation, vibrational data may indicate system agitation levels,properly operating equipment, or the like, and vibrational data belowamplitude and/or frequency thresholds may be an indication of a processthat is not operating according to expectations. Except where thecontext clearly indicates otherwise, any description herein describing adetermination of a value above a threshold and/or exceeding apredetermined or expected value is understood to include determinationof a value below a threshold and/or falling below a predetermined orexpected value.

The predetermined acceptable range may be based on anticipated systemresponse or vibration based on the equipment geometry and control schemesuch as number of bearings, relative rotational speed, influx of powerto the system at a certain frequency, and the like. The predeterminedacceptable range may also be based on long term analysis of detectionvalues across a plurality of similar equipment and components andcorrelation of data with equipment failure.

In some embodiments, an alert may be issued based on the some of thecriteria discussed above. In an illustrative example, an increase intemperature and energy at certain frequencies may indicate a hot bearingthat is starting to fail. In embodiments, the relative criteria for analarm may change with other data or information such as process stage,type of product being processed on equipment, ambient temperature andhumidity, external vibrations from other equipment and the like. In anillustrative and non-limiting example, the response circuit 8710 mayinitiate an alert if a vibrational amplitude and/or frequency exceeds apredetermined maximum value, if there is a change or rate of change thatexceeds a predetermined acceptable range, and/or if an accumulated valuebased on vibrational amplitude and/or frequency exceeds a threshold.

In embodiments, response circuit 8710 may cause the data acquisitioncircuit 8704 to enable or disable the processing of detection valuescorresponding to certain sensors based on the some of the criteriadiscussed above. This may include switching to sensors having differentresponse rates, sensitivity, ranges, and the like; accessing new sensorsor types of sensors, and the like. This switching may be implemented bychanging the control signals for the multiplexer circuit 8736 and/or byturning on or off certain input sections of the multiplexer circuit8736. The response circuit 8710 may make recommendations for thereplacement of certain sensors in the future with sensors havingdifferent response rates, sensitivity, ranges, and the like. Theresponse circuit 8710 may recommend design alterations for futureembodiments of the component, the piece of equipment, the operatingconditions, the process, and the like.

In embodiments, the response circuit 8710 may recommend maintenance atan upcoming process stop or initiate a maintenance call. The responsecircuit 8710 may recommend changes in process or operating parameters toremotely balance the piece of equipment. In embodiments, the responsecircuit 8710 may implement or recommend process changes—for example tolower the utilization of a component that is near a maintenanceinterval, operating off-nominally, or failed for purpose but still atleast partially operational. In an illustrative example, vibration phaseinformation, derived by the phase detection circuit 8712 relative to atimer signal from the timer circuit 8714, may be indicative of aphysical location of a problem. Based on the vibration phaseinformation, system design flaws, off-nominal operation, and/orcomponent or process failures may be identified.

In embodiments, based on relevant operating conditions and/or failuremodes which may occur in as sensor values approach one or more criteria,the signal evaluation circuit 8708 may store data in the data storagecircuit 8716 based on the fit of data relative to one or more criteria.Based on one sensor input meeting or approaching specified criteria orrange, the signal evaluation circuit 8708 may store additional data suchas RPMs, component loads, temperatures, pressures, vibrations in thedata storage circuit 8716. The signal evaluation circuit 8708 may storedata at a higher data rate for greater granularity in future processing,the ability to reprocess at different sampling rates, and/or to enablediagnosing or post-processing of system information where operationaldata of interest is flagged, and the like.

In embodiments, as shown in FIGS. 72 and 73 and 74 and 75, a datamonitoring system 8762 may include at least one data monitoring device8768. The at least one data monitoring device 8768 may include thesensors 8706 and a controller 8770 comprising the data acquisitioncircuit 8704, a signal evaluation circuit 8772, a data storage circuit8742, and a communications circuit 8752 to allow data and analysis to betransmitted to a monitoring application 8776 on a remote server 8774.The signal evaluation circuit 8772 may include at least one of a phasedetection circuit 8712 and a timer circuit 8714. The signal evaluationcircuit 8772 may periodically share data with the communication circuit8752 for transmittal to the remote server 8774 to enable the tracking ofcomponent and equipment performance over time and under varyingconditions by the monitoring application 8776. Because relevantoperating conditions and/or failure modes may occur as sensor valuesapproach one or more criteria, the signal evaluation circuit 8708 mayshare data with the communication circuit 8752 for transmittal to theremote server 8774 based on the fit of data relative to one or morecriteria. Based on one sensor input meeting or approaching specifiedcriteria or range, the signal evaluation circuit 8708 may shareadditional data such as RPMs, component loads, temperatures, pressures,vibrations, and the like for transmittal. The signal evaluation circuit8772 may share data at a higher data rate for transmittal to enablegreater granularity in processing on the remote server.

In embodiments, as shown in FIG. 72, the communications circuit 8752 maycommunicated data directly to the remote server 8774. In embodiments, asshown in FIG. 73, the communications circuit 8752 may communicate datato an intermediate computer 8754 which may include a processor 8756running an operating system 8758 and a data storage circuit 8760. Theintermediate computer 8754 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server8774.

In embodiments as illustrated in FIGS. 74 and 75, the data collectionsystem 8762 may have a plurality of monitoring devices 8768 collectingdata on multiple components in a single piece of equipment, collectingdata on the same component across a plurality of pieces of equipment,(both the same and different types of equipment) in the same facility aswell as collecting data from monitoring devices in multiple facilities.In embodiments, as show in FIG. 74 the communications circuit 8752 maycommunicated data directly to the remote server 8774. In embodiments, asshown in FIG. 75, the communications circuit 8752 may communicate datato the intermediate computer 8754 which may include the processor 8756running the operating system 8758 and a data storage circuit 8760. Theintermediate computer 8754 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server8774.

In embodiments, the monitoring application 8776 on the remote server8774 may receive and store one or more of detection values, timingsignals and data coming from a plurality of the various monitoringdevices 8768. The monitoring application 8776 may then select subsets ofthe detection values, timing signals and data to be jointly analyzed.Subsets for analysis may be selected based on a single type of componentor a single type of equipment in which a component is operating. Subsetsfor analysis may be selected or grouped based on common operatingconditions such as size of load, operational condition (e.g.,intermittent, continuous, process stage), operating speed or tachometer,common ambient environmental conditions such as humidity, temperature,air or fluid particulate, and the like. Subsets for analysis may beselected based on the effects of other nearby equipment such as nearbymachines rotating at similar frequencies.

The monitoring application 8776 may then analyze the selected subset. Inan illustrative example, data from a single component may be analyzedover different time periods such as one operating cycle, severaloperating cycles, a month, a year, the life of the component or thelike. Data from multiple components of the same type may also beanalyzed over different time periods. Trends in the data such as changesin frequency or amplitude may be correlated with failure and maintenancerecords associated with the same or a related component or piece ofequipment. Trends in the data such as changing rates of changeassociated with start-up or different points in the process may beidentified. Additional data may be introduced into the analysis such asoutput product quality, indicated success or failure of a process, andthe like. Correlation of trends and values for different types of datamay be analyzed to identify those parameters whose short-term analysismight provide the best prediction regarding expected performance. Thisinformation may be transmitted back to the monitoring device to updatetypes of data collected and analyzed locally or to influence the designof future monitoring devices.

In an illustrative and non-limiting example, the monitoring device 8768may be used to collect and process sensor data to measure mechanicaltorque. The monitoring device 8768 may be in communication with orinclude a high resolution, high speed vibration sensor to collect dataover a period of time sufficient to measure multiple cycles of rotation.For gear driven components, the sampling resolution of the sensor shouldbe such that the number of samples taken per cycle is at least equal tothe number of gear teeth driving the component. It will be understoodthat a lower sampling resolution may also be utilized, which may resultin a lower confidence determination and/or taking data over a longerperiod of time to develop sufficient statistical confidence. This datamay then be used in the generation of a phase reference (relative probe)or tachometer signal for a piece of equipment. This phase reference maybe used directly or used by the timer circuit 8714 to generate a timingsignal to align phase data such as vibrational data or acceleration datafrom multiple sensors located at different positions on a component oron different components within a system. This information may facilitatethe determination of torque for different components or the generationof an Operational Deflection Shape (ODS).

A higher resolution data stream may also provide additional data for thedetection of transitory signals in low speed operations. Theidentification of transitory signals may enable the identification ofdefects in a piece of equipment or component operating a low RPMs.

In an illustrative and non-limiting example, the monitoring device maybe used to identify mechanical jitter for use in failure predictionmodels. The monitoring device may begin acquiring data when the piece ofequipment starts up, through ramping up to operating speed, and thenduring operation. Once at operating speed, it is anticipated that thetorsional jitter should be minimal or within expected ranges, andchanges in torsion during this phase may be indicative of cracks,bearing faults, and the like. Additionally, known torsions may beremoved from the signal to facilitate in the identification ofunanticipated torsions resulting from system design flaws, componentwear, or unexpected process events. Having phase information associatedwith the data collected at operating speed may facilitate identificationof a location of vibration and potential component wear, and/or may befurther correlated to a type of failure for a component. Relative phaseinformation for a plurality of sensors located throughout a machine mayfacilitate the evaluation of torsion as it is propagated through a pieceof equipment.

In embodiments, the monitoring application 8776 may have access toequipment specifications, equipment geometry, component specifications,component materials, anticipated state information for plurality ofcomponent types, operational history, historical detection values,component life models, and the like for use in analyzing the selectedsubset using rule-based or model-based analysis. In embodiments, themonitoring application 8776 may feed a neural net with the selectedsubset to learn to recognize various operating state, health states(e.g., lifetime predictions) and fault states utilizing deep learningtechniques. In embodiments, a hybrid of the two techniques (model-basedlearning and deep learning) may be used.

In an illustrative and non-limiting example, component health of:conveyors and lifters in an assembly line; water pumps on industrialvehicles; factory air conditioning units; drilling machines, screwdrivers, compressors, pumps, gearboxes, vibrating conveyors, mixers andmotors situated in the oil and gas fields; factory mineral pumps;centrifuges, and refining tanks situated in oil and gas refineries; andcompressors in gas handling systems may be monitored using the phasedetection and alignment techniques, data monitoring devices and datacollection systems described herein.

In an illustrative and non-limiting example, the component health ofequipment to promote chemical reactions deployed in chemical andpharmaceutical production lines (e.g., rotating tank/mixer agitators,mechanical/rotating agitators, and propeller agitators) may be evaluatedusing the phase detection and alignment techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the component health ofvehicle steering mechanisms and/or vehicle engines may be evaluatedusing the phase detection and alignment techniques, data monitoringdevices and data collection systems described herein.

An example monitoring system for data collection, includes a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors communicatively coupled to thedata acquisition circuit; a signal evaluation circuit comprising: atimer circuit structured to generate at least one timing signal; and aphase detection circuit structured to determine a relative phasedifference between at least one of the plurality of detection values andat least one of the timing signals from the timer circuit; and aresponse circuit structured to perform at least one operation inresponse to the relative phase difference. In certain furtherembodiments, an example system includes: the at least one operation isfurther in response to at least one of: a change in amplitude of atleast one of the plurality of detection values; a change in frequency orrelative phase of at least one of the plurality of detection values; arate of change in both amplitude and relative phase of at least one theplurality of detection values; and a relative rate of change inamplitude and relative phase of at least one the plurality of detectionvalues. In embodiments, the at least one operation comprises issuing analert. In embodiments, the alert may be one of haptic, audible andvisual; a data storage circuit. In embodiments, the relative phasedifference and at least one of the detection values and the timingsignal are stored. In embodiments, the at least one operation furthercomprises storing additional data in the data storage circuit. Inembodiments, the storing additional data in the data storage circuit isfurther in response to at least one of: a change in the relative phasedifference and a relative rate of change in the relative phasedifference. In embodiments, the data acquisition circuit furthercomprises at least one multiplexer circuit (MUX) whereby alternativecombinations of detection values may be selected based on at least oneof user input and a selected operating parameter for a machine. Inembodiments, each of the plurality of detection values corresponds to atleast one of the input sensors. In embodiments, the at least oneoperation comprises enabling or disabling one or more portions of themultiplexer circuit, or altering the multiplexer control lines. Inembodiments, the data acquisition circuit comprises at least twomultiplexer circuits and the at least one operation comprises changingconnections between the at least two multiplexer circuits; and/or thesystem further comprising a MUX control circuit structured to interpreta subset of the plurality of detection values and provide the logicalcontrol of the MUX and the correspondence of MUX input and detectedvalues as a result. In embodiments, the logic control of the MUXcomprises adaptive scheduling of the select lines.

An example system for data collection, includes: a data acquisitioncircuit structured to interpret a plurality of detection values, each ofthe plurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a signal evaluation circuit comprising: a timercircuit structured to generate a timing signal based on a first detectedvalue of the plurality of detection values; and a phase detectioncircuit structured to determine a relative phase difference between asecond detection value of the plurality of detection values and thetiming signal; and a phase response circuit structured to perform atleast one operation in response to the phase difference. In certainfurther embodiments, an example system includes the at least oneoperation is further in response to at least one of: a change inamplitude of at least one of the plurality of detection values; a changein frequency or relative phase of at least one of the plurality ofdetection values; a rate of change in both amplitude and relative phaseof at least one the plurality of detection values and a relative rate ofchange in amplitude and relative phase of at least one the plurality ofdetection values. In embodiments, the at least one operation comprisesissuing an alert. In embodiments, the alert may be one of haptic,audible and visual; where the system, further includes a data storagecircuit. In embodiments, the relative phase difference and at least oneof the detection values and the timing signal are stored. Inembodiments, the at least one operation further includes storingadditional data in the data storage circuit. In embodiments, the storingadditional data in the data storage circuit is further in response to atleast one of: a change in the relative phase difference and a relativerate of change in the relative phase difference. In embodiments, thedata acquisition circuit further includes at least one multiplexer (MUX)circuit whereby alternative combinations of detection values may beselected based on at least one of user input and a selected operatingparameter for a machine. In embodiments, each of the plurality ofdetection values corresponds to at least one of the input sensors. Inembodiments, the at least one operation comprises enabling or disablingone or more portions of the multiplexer circuit, or altering themultiplexer control lines. In embodiments, the data acquisition circuitcomprises at least two multiplexer circuits and the at least oneoperation comprises changing connections between the at least twomultiplexer circuits; where the system further comprising a MUX controlcircuit structured to interpret a subset of the plurality of detectionvalues and provide the logical control of the MUX and the correspondenceof MUX input and detected values as a result; and/or the logic controlof the MUX comprises adaptive scheduling of the select lines.

An example system for data collection, processing, and utilization ofsignals in an industrial environment includes a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a signal evaluation circuit comprising: a timercircuit structured to generate a timing signal based on a first detectedvalue of the plurality of detection values; and a phase detectioncircuit structured to determine a relative phase difference between asecond detection value of the plurality of detection values and thetiming signal; a data storage facility for storing a subset of theplurality of detection values and the timing signal; a communicationcircuit structured to communicate at least one selected detection valueand the timing signal to a remote server; and a monitoring applicationon the remote server structured to receive the at least one selecteddetection value and the timing signal; jointly analyze a subset of thedetection values received from the plurality of monitoring devices; andrecommend an action. In certain embodiments, the joint analysiscomprises using the timing signal from each of the plurality ofmonitoring devices to align the detection values from the plurality ofmonitoring devices. In embodiments, the subset of detection values isselected based on data associated with a detection value comprising atleast one: common type of component, common type of equipment, andcommon operating conditions.

An example system for data collection in an industrial environment,includes: a data acquisition circuit structured to interpret a pluralityof detection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensorscommunicatively coupled to the data acquisition circuit, the dataacquisition circuit comprising a multiplexer circuit whereby alternativecombinations of the detection values may be selected based on at leastone of user input, a detected state and a selected operating parameterfor a machine, each of the plurality of detection values correspondingto at least one of the input sensors; a signal evaluation circuitcomprising: a timer circuit structured to generate a timing signal; anda phase detection circuit structured to determine a relative phasedifference between at least one of the plurality of detection values anda signal from the timer circuit; and a response circuit structured toperform at least one operation in response to the phase difference.

An example monitoring system for data collection in a piece ofequipment, includes a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensorscommunicatively coupled to the data acquisition circuit; a timer circuitstructured to generate a timing signal based on a first detected valueof the plurality of detection values; a signal evaluation circuitstructured to obtain at least one of vibration amplitude, vibrationfrequency and vibration phase location corresponding to a seconddetected value comprising: a phase detection circuit structured todetermine a relative phase difference between a second detection valueof the plurality of detection values and the timing signal; and aresponse circuit structured to perform at least one operation inresponse to at the at least one of the vibration amplitude, vibrationfrequency and vibration phase location.

A monitoring system for bearing analysis in an industrial environment,the monitoring device includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a timercircuit structured to generate a timing signal a data storage forstoring specifications and anticipated state information for a pluralityof bearing types and buffering the plurality of detection values for apredetermined length of time; a timer circuit structured to generate atiming signal based on a first detected value of the plurality ofdetection values; a bearing analysis circuit structured to analyzebuffered detection values relative to specifications and anticipatedstate information resulting in a life prediction comprising: a phasedetection circuit structured to determine a relative phase differencebetween a second detection value of the plurality of detection valuesand the timing signal; a signal evaluation circuit structured to obtainat least one of vibration amplitude, vibration frequency and vibrationphase location corresponding to a second detected value: and a responsecircuit structured to perform at least one operation in response to atthe at least one of the vibration amplitude, vibration frequency andvibration phase location.

In embodiments, information about the health or other status or stateinformation of or regarding a component or piece of industrial equipmentmay be obtained by monitoring the condition of various componentsthroughout a process. Monitoring may include monitoring the amplitude ofa sensor signal measuring attributes such as temperature, humidity,acceleration, displacement and the like. An embodiment of a datamonitoring device 9000 is shown in FIG. 76 and may include a pluralityof the sensors 9006 communicatively coupled to a controller 9002. Thecontroller 9002, which may be part of a data collection device, such asa mobile data collector, or part of a system, such as a network-deployedor cloud-deployed system, may include a data acquisition circuit 9004, asignal evaluation circuit 9008 and a response circuit 9010. The signalevaluation circuit 9008 may comprise the peak detection circuit 9012.Additionally, the signal evaluation circuit 9008 may optionally compriseone or more of a phase detection circuit 9016, a bandpass filter circuit9018, a phase lock loop circuit, a torsional analysis circuit, a bearinganalysis circuit, and the like. The bandpass filter 9018 may be used tofilter a stream of detection values such that values, such as peaks andvalleys, are detected only at or within bands of interest, such asfrequencies of interest. The data acquisition circuit 9004 may includeone or more analog-to-digital converter circuits 9014. A peak amplitudedetected by the peak detection circuit 9012 may be input into one ormore of the analog-to-digital converter circuits 9014 to provide areference value for scaling output of the analog-to-digital convertercircuits 9014 appropriately.

The plurality of sensors 9006 may be wired to ports on the dataacquisition circuit 9004. The plurality of sensors 9006 may bewirelessly connected to the data acquisition circuit 9004. The dataacquisition circuit 9004 may be able to access detection valuescorresponding to the output of at least one of the plurality of thesensors 9006 where the sensors 9006 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.

The selection of the plurality of the sensors 9006 for the datamonitoring device 9000 designed for a specific component or piece ofequipment may depend on a variety of considerations such asaccessibility for installing new sensors, incorporation of sensors inthe initial design, anticipated operational and failure conditions,resolution desired at various positions in a process or plant,reliability of the sensors, power availability, power utilization,storage utilization, and the like. The impact of a failure, timeresponse of a failure (e.g., warning time and/or off-optimal modesoccurring before failure), likelihood of failure, extent of impact offailure, and/or sensitivity required and/or difficulty to detectionfailure conditions may drive the extent to which a component or piece ofequipment is monitored with more sensors and/or higher capabilitysensors being dedicated to systems where unexpected or undetectedfailure would be costly or have severe consequences.

The signal evaluation circuit 9008 may process the detection values toobtain information about a component or piece of equipment beingmonitored. Information extracted by the signal evaluation circuit 9008may comprise information regarding a peak value of a signal such as apeak temperature, peak acceleration, peak velocity, peak pressure, peakweight bearing, peak strain, peak bending, or peak displacement. Thepeak detection may be done using analog or digital circuits. Inembodiments, the peak detection circuit 9012 may be able to distinguishbetween “local” or short term peaks in a stream of detection values anda “global” or longer term peak. In embodiments, the peak detectioncircuit 9012 may be able to identify peak shapes (not just a single peakvalue) such as flat tops, asymptotic approaches, discrete jumps in thepeak value or rapid/steep climbs in peak value, sinusoidal behaviorwithin ranges and the like. Flat topped peaks may indicate saturation atof a sensor. Asymptotic approaches to a peak may indicate linear systembehavior. Discrete jumps in value or steep changes in peak value mayindicate quantized or nonlinear behavior of either the sensor doing themeasurement or the behavior of the component. In embodiments, the systemmay be able to identify sinusoidal variations in the peak value withinan envelope, such as an envelope established by line or curve connectinga series of peak values. It should be noted that references to “peaks”should be understood to encompass one or more “valleys,” representing aseries of low points in measurement, except where context indicatesotherwise.

In embodiments, a peak value may be used as a reference for theanalog-to-digital conversion circuit 9014.

In an illustrative and non-limiting example, a temperature probe maymeasure the temperature of a gear as it rotates in a machine. The peaktemperature may be detected by the peak detection circuit 9012. The peaktemperature may be fed into the analog-to-digital converter circuit 9014to appropriately scale a stream of detection values corresponding totemperature readings of the gear as it rotates in a machine. The phaseof the stream of detection values corresponding to temperature relativeto an orientation of the gear may be determined by the phase detectioncircuit 9016. Knowing where in the rotation of the gear a peaktemperature is occurring may allow the identification of a bad geartooth.

In some embodiments, two or more sets of detection values may be fusedto create detection values for a virtual sensor. A peak detectioncircuit may be used to verify consistency in timing of peak valuesbetween at least one of the two or more sets of detection values and thedetection values for the virtual sensor.

In embodiments, the signal evaluation circuit 9008 may be able to resetthe peak detection circuit 9012 upon start-up of the monitoring device9000, upon edge detection of a control signal of the system beingmonitored, based on a user input, after a system error and the like. Inembodiments, the signal evaluation circuit 9008 may discard an initialportion of the output of the peak detection circuit 9012 prior to usingthe peak value as a reference value for an analog-to-digital conversioncircuit to allow the system to fully come on line.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, thesensors 9006 may comprise one or more of, without limitation, avibration sensor, a thermometer, a hygrometer, a voltage sensor, acurrent sensor, an accelerometer, a velocity detector, a light orelectromagnetic sensor (e.g., determining temperature, compositionand/or spectral analysis, and/or object position or movement), an imagesensor, a structured light sensor, a laser-based image sensor, anacoustic wave sensor, a displacement sensor, a turbidity meter, aviscosity meter, a load sensor, a tri-axial sensor, an accelerometer, atachometer, a fluid pressure meter, an air flow meter, a horsepowermeter, a flow rate meter, a fluid particle detector, an acousticalsensor, a pH sensor, and the like, including, without limitation, any ofthe sensors described throughout this disclosure and the documentsincorporated by reference.

The sensors 9006 may provide a stream of data over time that has a phasecomponent, such as relating to acceleration or vibration, allowing forthe evaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors9006 may provide a stream of data that is not conventionallyphase-based, such as temperature, humidity, load, and the like. Thesensors 9006 may provide a continuous or near continuous stream of dataover time, periodic readings, event-driven readings, and/or readingsaccording to a selected interval or schedule.

In embodiments, as illustrated in FIG. 76, the sensors 9006 may be partof the data monitoring device 9000, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 77 and 78, oneor more external sensors 9026, which are not explicitly part of amonitoring device 9020 but rather are new, previously attached to orintegrated into the equipment or component, may be opportunisticallyconnected to or accessed by the monitoring device 9020. The monitoringdevice 9020 may include a controller 9022. The controller 9022 mayinclude the response circuit 9010, the signal evaluation circuit 9008and the data acquisition circuit 9024. The signal evaluation circuit9008 may include the peak detection circuit 9012 and optionally thephase detection circuit 9016 and/or the bandpass filter circuit 9018.The data acquisition circuit 9024 may include one or more input ports9028. The one or more of the external sensors 9026 may be directlyconnected to the one or more of the input ports 9028 on the dataacquisition circuit 9024 of the controller 9022 or may be accessed bythe data acquisition circuit 9004 wirelessly, such as by a reader,interrogator, or other wireless connection, such as over ashort-distance wireless protocol. In embodiments as shown in FIG. 78,the data acquisition circuit 9024 may further comprise a wirelesscommunication circuit 9030. The data acquisition circuit 9024 may usethe wireless communication circuit 9030 to access detection valuescorresponding to the one or more of the external sensors 9026 wirelesslyor via a separate source or some combination of these methods.

In embodiments as illustrated in FIG. 79, a data acquisition circuit9036 may further comprise a multiplexer circuit 9038 as describedelsewhere herein. Outputs from the multiplexer circuit 9038 may beutilized by the signal evaluation circuit 9008. The response circuit9010 may have the ability to turn on and off portions of the multiplexorcircuit 9038. The response circuit 9010 may have the ability to controlthe control channels of the multiplexor circuit 9038

The response circuit 9010 may evaluate the results of the signalevaluation circuit 9008 and, based on certain criteria, initiate anaction. The criteria may include a predetermined peak value for adetection value from a specific sensor, a cumulative value of a sensor'scorresponding detection value over time, a change in peak value, a rateof change in a peak value, and/or an accumulated value (e.g., a timespent above/below a threshold value, a weighted time spent above/belowone or more threshold values, and/or an area of the detected valueabove/below one or more threshold values). The criteria may comprisecombinations of data from different sensors such as relative values,relative changes in value, relative rates of change in value, relativevalues over time, and the like. The relative criteria may change withother data or information such as process stage, type of product beingprocessed, type of equipment, ambient temperature and humidity, externalvibrations from other equipment, and the like. The relative criteria maybe reflected in one or more calculated statistics or metrics (includingones generated by further calculations on multiple criteria orstatistics), which in turn may be used for processing (such as anon-board a data collector or by an external system), such as to beprovided as an input to one or more of the machine learning capabilitiesdescribed in this disclosure, to a control system (which may be on-boarda data collector or remote, such as to control selection of data inputs,multiplexing of sensor data, storage, or the like), or as a data elementthat is an input to another system, such as a data stream or datapackage that may be available to a data marketplace, a SCADA system, aremote control system, a maintenance system, an analytic system, orother system.

Certain embodiments are described herein as detected values exceedingthresholds or predetermined values, but detected values may also fallbelow thresholds or predetermined values—for example where an amount ofchange in the detected value is expected to occur, but detected valuesindicate that the change may not have occurred. For example, and withoutlimitation, vibrational data may indicate system agitation levels,properly operating equipment, or the like, and vibrational data belowamplitude and/or frequency thresholds may be an indication of a processthat is not operating according to expectations. For example, in aprocess involving a blender, a mixer, an agitator or the like, theabsence of vibration may indicate that a blade, fin, vane or otherworking element is unable to move adequately, such as, for example, as aresult of a working material being excessively viscous or as a result ofa problem in gears (e.g., stripped gears, seizing in gears, or the like(a clutch, or the like). Except where the context clearly indicatesotherwise, any description herein describing a determination of a valueabove a threshold and/or exceeding a predetermined or expected value isunderstood to include determination of a value below a threshold and/orfalling below a predetermined or expected value.

The predetermined acceptable range may be based on anticipated systemresponse or vibration based on the equipment geometry and control schemesuch as number of bearings, relative rotational speed, influx of powerto the system at a certain frequency, and the like. The predeterminedacceptable range may also be based on long term analysis of detectionvalues across a plurality of similar equipment and components andcorrelation of data with equipment failure.

In embodiments, the response circuit 9010 may issue an alert based onone or more of the criteria discussed above. In an illustrative example,an increase in peak temperature beyond a predetermined value mayindicate a hot bearing that is starting to fail. In embodiments, therelative criteria for an alarm may change with other data or informationsuch as process stage, type of product being processed on equipment,ambient temperature and humidity, external vibrations from otherequipment and the like. In an illustrative and non-limiting example, theresponse circuit 9010 may initiate an alert if an amplitude, such as avibrational amplitude and/or frequency, exceeds a predetermined maximumvalue, if there is a change or rate of change that exceeds apredetermined acceptable range, and/or if an accumulated value based onsuch amplitude and/or frequency exceeds a threshold.

In embodiments, the response circuit 9010 may cause the data acquisitioncircuit 9004 to enable or disable the processing of detection valuescorresponding to certain sensors based on one or more of the criteriadiscussed above. This may include switching to sensors having differentresponse rates, sensitivity, ranges, and the like; accessing new sensorsor types of sensors, accessing data from multiple sensors, and the like.Switching may be based on a detected peak value for the sensor beingswitched or based on the peak value of another sensor. Switching may beundertaken based on a model, a set of rules, or the like. Inembodiments, switching may be under control of a machine learningsystem, such that switching is controlled based on one or more metricsof success, combined with input data, over a set of trials, which mayoccur under supervision of a human supervisor or under control of anautomated system. Switching may involve switching from one input port toanother (such as to switch from one sensor to another). Switching mayinvolve altering the multiplexing of data, such as combining differentstreams under different circumstances. Switching may involve activatinga system to obtain additional data, such as moving a mobile system (suchas a robotic or drone system), to a location where different oradditional data is available (such as positioning an image sensor for adifferent view or positioning a sonar sensor for a different directionof collection) or to a location where different sensors can be accessed(such as moving a collector to connect up to a sensor that is disposedat a location in an environment by a wired or wireless connection). Thisswitching may be implemented by changing the control signals for themultiplexor circuit 9038 and/or by turning on or off certain inputsections of the multiplexor circuit 9038.

In embodiments, the response circuit 9010 may adjust a sensor scalingvalue using the detected peak as a reference voltage. The responsecircuit 9010 may adjust a sensor sampling rate such that the peak valueis captured.

The response circuit 9010 may identify sensor overload. In embodiments,the response circuit 9010 may make recommendations for the replacementof certain sensors in the future with sensors having different responserates, sensitivity, ranges, and the like. The response circuit 9010 mayrecommend design alterations for future embodiments of the component,the piece of equipment, the operating conditions, the process, and thelike.

In embodiments, the response circuit 9010 may recommend maintenance atan upcoming process stop or initiate a maintenance call where themaintenance may include the replacement of the sensor with the same oran alternate type of sensor having a different response rate,sensitivity, range and the like. In embodiments, the response circuit9010 may implement or recommend process changes—for example, to lowerthe utilization of a component that is near a maintenance interval,operating off-nominally, or failed for purpose but still at leastpartially operational, to change the operating speed of a component(such as to put it in a lower-demand mode), to initiate amelioration ofan issue (such as to signal for additional lubrication of a rollerbearing set, or to signal for an alignment process for a system that isout of balance), and the like.

In embodiments, as shown in FIG. 80, a data monitoring device 9040 mayinclude the sensors 9006 and a controller 9042 which may include thedata acquisition circuit 9004, and the signal evaluation circuit 9008.The signal evaluation circuit 9008 may include the peak detectioncircuit 9012 and, optionally, the phased detection circuit 9016 and/orthe bandpass filter circuit 9018. The controller 9042 may furtherinclude a data storage circuit 9044, memory, and the like. Thecontroller 9042 may further include the response circuit 9010. Thesignal evaluation circuit 9008 may periodically store certain detectionvalues in the data storage circuit 9044 to enable the tracking ofcomponent performance over time.

In embodiments, based on relevant criteria as described elsewhereherein, operating conditions and/or failure modes which may occur assensor values approach one or more criteria, the signal evaluationcircuit 9008 may store data in the data storage circuit 9044 based onthe fit of data relative to one or more criteria, such as thosedescribed throughout this disclosure. Based on one sensor input meetingor approaching specified criteria or range, the signal evaluationcircuit 9008 may store additional data such as RPMs, component loads,temperatures, pressures, vibrations or other sensor data of the typesdescribed throughout this disclosure in a data storage circuit 9068. Thesignal evaluation circuit 9008 may store data at a higher data rate forgreater granularity in future processing, the ability to reprocess atdifferent sampling rates, and/or to enable diagnosing or post-processingof system information where operational data of interest is flagged, andthe like.

In embodiments, the signal evaluation circuit 9008 may store new peaksthat indicate changes in overall scaling over a long duration (e.g.,scaling a data stream based on historical peaks over months ofanalysis). The signal evaluation circuit 9008 may store data whenhistorical peak values are approached (e.g., as temperatures, pressures,vibrations, velocities, accelerations and the like approach historicalpeaks).

In embodiments as shown in FIGS. 81 and 82 and 83 and 84, a datamonitoring system 9046 may include at least one data monitoring device9048. At least one of the data monitoring device 9048 may include thesensors 9006 and a controller 9050 comprising the data acquisitioncircuit 9004, the signal evaluation circuit 9008, the data storagecircuit 9044, and a communication circuit 9052 to allow data andanalysis to be transmitted to a monitoring application 9056 on a remoteserver 9054. The signal evaluation circuit 9008 may include at least oneof the peak detection circuit 9012. The signal evaluation circuit 9008may periodically share data with the communication circuit 9052 fortransmittal to the remote server 9054 to enable the tracking ofcomponent and equipment performance over time and under varyingconditions by the monitoring application 9056. Because relevantoperating conditions and/or failure modes may occur as sensor valuesapproach one or more criteria as described elsewhere herein, the signalevaluation circuit 9008 may share data with the communication circuit9052 for transmittal to the remote server 9054 based on the fit of datarelative to one or more criteria. Based on one sensor input meeting orapproaching specified criteria or range, the signal evaluation circuit9008 may share additional data such as RPMs, component loads,temperatures, pressures, vibrations, and the like for transmittal. Thesignal evaluation circuit 9008 may share data at a higher data rate fortransmittal to enable greater granularity in processing on the remoteserver.

In embodiments, as shown in FIG. 81, the communication circuit 9052 maycommunicate data directly to the remote server 9054. In embodiments, asshown in FIG. 82, the communication circuit 9052 may communicate data toan intermediate computer 9058 which may include a processor 9060 runningan operating system 9062 and a data storage circuit 9064.

In embodiments, as illustrated in FIGS. 83 and 84, a data collectionsystem 9066 may have a plurality of the monitoring devices 9048collecting data on multiple components in a single piece of equipment,collecting data on the same component across a plurality of pieces ofequipment (both the same and different types of equipment) in the samefacility as well as collecting data from monitoring devices in multiplefacilities. The monitoring application 9056 on the remote server 9054may receive and store one or more of detection values, timing signals ordata coming from a plurality of the various monitoring devices 9048.

In embodiments, as shown in FIG. 81, the communication circuit 9052 maycommunicate data directly to the remote server 9054. In embodiments, asshown in FIG. 82, the communication circuit 9052 may communicate data tothe intermediate computer 9058 which may include the processor 9060running the operating system 9062 and the data storage circuit 9064.There may be the individual intermediate computer 9058 associated witheach of the monitoring device 9048 or the individual intermediatecomputer 9058 may be associated with a plurality of the monitoringdevices 9048 where the intermediate computer 9058 may collect data froma plurality of data monitoring devices and send the cumulative data tothe remote server 9054.

The monitoring application 9056 may select subsets of the detectionvalues, timing signals and data to be jointly analyzed. Subsets foranalysis may be selected based on a single type of component or a singletype of equipment in which a component is operating. Subsets foranalysis may be selected or grouped based on common operating conditionssuch as size of load, operational condition (e.g., intermittent,continuous), operating speed or tachometer, common ambient environmentalconditions such as humidity, temperature, air or fluid particulate, andthe like. Subsets for analysis may be selected based on the effects ofother nearby equipment such as nearby machines rotating at similarfrequencies, nearby equipment producing electromagnetic fields, nearbyequipment producing heat, nearby equipment inducing movement orvibration, nearby equipment emitting vapors, chemicals or particulates,or other potentially interfering or intervening effects.

The monitoring application 9056 may then analyze the selected subset. Inan illustrative example, data from a single component may be analyzedover different time periods such as one operating cycle, severaloperating cycles, a month, a year, the life of the component or thelike. Data from multiple components of the same type may also beanalyzed over different time periods. Trends in the data such as changesin frequency or amplitude may be correlated with failure and maintenancerecords associated with the same or a related component or piece ofequipment. Trends in the data, such as changing rates of changeassociated with start-up or different points in the process, may beidentified. Additional data may be introduced into the analysis such asoutput product quality, output quantity (such as per unit of time),indicated success or failure of a process, and the like. Correlation oftrends and values for different types of data may be analyzed toidentify those parameters whose short-term analysis might provide thebest prediction regarding expected performance. This information may betransmitted back to the monitoring device to update types of datacollected and analyzed locally or to influence the design of futuremonitoring devices.

In embodiments, the monitoring application 9056 may have access toequipment specifications, equipment geometry, component specifications,component materials, anticipated state information for a plurality ofcomponent types, operational history, historical detection values,component life models and the like for use analyzing the selected subsetusing rule-based or model-based analysis. In embodiments, the monitoringapplication 9056 may feed a neural net with the selected subset to learnto recognize peaks in waveform patterns by feeding a large data setsample of waveform behavior of a given type within which peaks aredesignated (such as by human analysts).

A monitoring system for data collection in an industrial environment,the monitoring system comprising: a data acquisition circuit structuredto interpret a plurality of detection values from a plurality of inputsensors communicatively coupled to the data acquisition circuit, each ofthe plurality of detection values corresponding to at least one of theinput sensors; a peak detection circuit structured to determine at leastone peak value in response to the plurality of detection values; and apeak response circuit structured to perform at least one operation inresponse to the at least one peak value.

An example monitoring system further includes the at least one operationthat is further in response to at least one of: a change in amplitude ofat least one of the plurality of detection values; a change in frequencyor relative phase of at least one of the plurality of detection values;a rate of change in both amplitude and relative phase of at least one ofthe plurality of detection values; and a relative rate of change inamplitude and relative phase of at least one of the plurality ofdetection values. In embodiments, the at least one operation comprisesissuing an alert. In embodiments, the alert may be one of haptic,audible or visual; further comprising a data storage circuit. Inembodiments, the relative phase difference and at least one of thedetection values and the timing signal are stored. In embodiments, theat least one operation further comprises storing additional data in thedata storage circuit. In embodiments, the storing additional data in thedata storage circuit is further in response to at least one of: a changein the relative phase difference and a relative rate of change in therelative phase difference. In embodiments, the data acquisition circuitfurther comprises at least one multiplexer circuit whereby alternativecombinations of detection values may be selected based on at least oneof user input and a selected operating parameter for a machine. Inembodiments, each of the plurality of detection values corresponds to atleast one of the input sensors. In embodiments, the at least oneoperation comprises enabling or disabling one or more portions of themultiplexer circuit, or altering the multiplexer control lines. Inembodiments, the data acquisition circuit comprises at least twomultiplexer circuits and the at least one operation comprises changingconnections between the at least two multiplexer circuits.

A monitoring system for data collection in an industrial environment,the monitoring system structure to receive input corresponding to aplurality of sensors, includes a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input sensors; apeak detection circuit structured to determine at least one peak valuein response to the plurality of detection values; and a peak responsecircuit structured to perform at least one operation in response to theat least one peak value.

An example monitoring system further includes the at least one operationthat is further in response to at least one of: a change in amplitude ofat least one of the plurality of detection values; a change in frequencyor relative phase of at least one of the plurality of detection values;a rate of change in both amplitude and relative phase of at least one ofthe plurality of detection values; and a relative rate of change inamplitude and relative phase of at least one of the plurality ofdetection values. In embodiments, at least one operation comprisesissuing an alert. In embodiments, alert may be one of haptic, audible orvisual further comprising a data storage circuit. In embodiments, therelative phase difference and at least one of the detection values andthe timing signal are stored. In embodiments, the at least one operationfurther comprises storing additional data in the data storage circuit.In embodiments, the storing additional data in the data storage circuitis further in response to at least one of: a change in the relativephase difference and a relative rate of change in the relative phasedifference. In embodiments, the data acquisition circuit furthercomprises at least one multiplexer circuit whereby alternativecombinations of detection values may be selected based on at least oneof user input and a selected operating parameter for a machine. Inembodiments, each of the plurality of detection values corresponds to atleast one of the input sensors. In embodiments, the at least oneoperation comprises enabling or disabling one or more portions of themultiplexer circuit, or altering the multiplexer control lines. Inembodiments, the data acquisition circuit comprises at least twomultiplexer circuits and the at least one operation comprises changingconnections between the at least two multiplexer circuits.

An example system for data collection, processing, and utilization ofsignals in an industrial environment includes: a plurality of monitoringdevices, each monitoring device comprising: a data acquisition circuitstructured to interpret a plurality of detection values from a pluralityof input sensors communicatively coupled to the data acquisitioncircuit, each of the plurality of detection values corresponding to atleast one of the input sensors; a peak detection circuit structured todetermine at least one peak value in response to the plurality ofdetection values; a peak response circuit structured to select at leastone detection value in response to the at least one peak value; acommunication circuit structured to communicate the at least oneselected detection value to a remote server; and a monitoringapplication on the remote server structured to: receive the at least oneselected detection value; jointly analyze received detection values froma subset of the plurality of monitoring devices; and recommend anaction.

An example system further includes: the system further structured tosubset detection values based on one of anticipated life of a componentassociated with detection values, type of the equipment associated withdetection values, and operational conditions under which detectionvalues were measured. In embodiments, the analysis of the subset ofdetection values comprises feeding a neural net with the subset ofdetection values and supplemental information to learn to recognizevarious operating states, health states, life expectancies and faultstates utilizing deep learning techniques. In embodiments, thesupplemental information comprises one of component specification,component performance, equipment specification, equipment performance,maintenance records, repair records and an anticipated state model. Inembodiments, the at least one operation is further in response to atleast one of: a change in amplitude of at least one of the plurality ofdetection values; a change in frequency or relative phase of at leastone of the plurality of detection values; a rate of change in bothamplitude and relative phase of at least one the plurality of detectionvalues; and a relative rate of change in amplitude and relative phase ofat least one the plurality of detection values. In embodiments, the atleast one operation comprises issuing an alert. In embodiments, thealert may be one of haptic, audible and visual further comprising a datastorage circuit. In embodiments, the relative phase difference and atleast one of the detection values and the timing signal are stored. Inembodiments, the at least one operation further comprises storingadditional data in the data storage circuit. In embodiments, the storingadditional data in the data storage circuit is further in response to atleast one of: a change in the relative phase difference and a relativerate of change in the relative phase difference. In embodiments, thedata acquisition circuit further comprises at least one multiplexercircuit whereby alternative combinations of detection values may beselected based on at least one of user input and a selected operatingparameter for a machine. In embodiments, each of the plurality ofdetection values corresponds to at least one of the input sensors. Inembodiments, the at least one operation comprises enabling or disablingone or more portions of the multiplexer circuit, or altering themultiplexer control lines and/or. In embodiments, the data acquisitioncircuit comprises at least two multiplexer circuits and the at least oneoperation comprises changing connections between the at least twomultiplexer circuits.

An example motor monitoring system, includes: a data acquisition circuitstructured to interpret a plurality of detection values from a pluralityof input sensors communicatively coupled to the data acquisitioncircuit, each of the plurality of detection values corresponding to atleast one of the input sensors; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor the motor and motor components, store historical motor performanceand buffer the plurality of detection values for a predetermined lengthof time; a peak detection circuit structured to determine a plurality ofpeak values comprising at least a temperature peak value, a speed peakvalue and a vibration peak value in response to the plurality ofdetection values and analyze the peak values relative to buffereddetection values, specifications and anticipated state informationresulting in a motor performance parameter; and a peak response circuitstructured to perform at least one operation in response to one of apeak value and a motor system performance parameter.

An example system for estimating a vehicle steering system performanceparameter, the device includes: a data acquisition circuit structured tointerpret a plurality of detection values from a plurality of inputsensors communicatively coupled to the data acquisition circuit, each ofthe plurality of detection values corresponding to at least one of theinput sensors; a data storage circuit structured to storespecifications, system geometry, and anticipated state information forthe vehicle steering system, the rack, the pinion, and the steeringcolumn, store historical steering system performance and buffer theplurality of detection values for a predetermined length of time; a peakdetection circuit structured to determine a plurality of peak valuescomprising at least a temperature peak value, a speed peak value and avibration peak value in response to the plurality of detection valuesand analyze the peak values relative to buffered detection values,specifications and anticipated state information resulting in a vehiclesteering system performance parameter; and a peak response circuitstructured to perform at least one operation in response to one of apeak value and a vehicle steering system performance parameter.

An example system for estimating a pump performance parameter, includes:a data acquisition circuit structured to interpret a plurality ofdetection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for the pump and pumpcomponents associated with the detection values, store historical pumpperformance and buffer the plurality of detection values for apredetermined length of time; a peak detection circuit structured todetermine a plurality of peak values comprising at least a temperaturepeak value, a speed peak value and a vibration peak value in response tothe plurality of detection values and analyze the peak values relativeto buffered detection values, specifications and anticipated stateinformation resulting in a pump performance parameter; and a peakresponse circuit structured to perform at least one operation inresponse to one of a peak value and a pump performance parameter. Incertain further embodiments, the example system includes. Inembodiments, the pump is a water pump in a car and. In embodiments, thepump is a mineral pump.

An example system for estimating a drill performance parameter for adrilling machine, includes a data acquisition circuit structured tointerpret a plurality of detection values from a plurality of inputsensors communicatively coupled to the data acquisition circuit, each ofthe plurality of detection values corresponding to at least one of theinput sensors; a data storage circuit structured to storespecifications, system geometry, and anticipated state information forthe drill and drill components associated with the detection values,store historical drill performance and buffer the plurality of detectionvalues for a predetermined length of time; a peak detection circuitstructured to determine a plurality of peak values comprising at least atemperature peak value, a speed peak value and a vibration peak value inresponse to the plurality of detection values and analyze the peakvalues relative to buffered detection values, specifications andanticipated state information resulting in a drill performanceparameter; and a peak response circuit structured to perform at leastone operation in response to one of a peak value and a drill performanceparameter. In embodiments, the drilling machine is one of an oildrilling machine and a gas drilling machine.

An example system for estimating a conveyor health parameter, the systemincludes: a data acquisition circuit structured to interpret a pluralityof detection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for a conveyor and conveyorcomponents associated with the detection values, store historicalconveyor performance and buffer the plurality of detection values for apredetermined length of time; a peak detection circuit structured todetermine a plurality of peak values comprising at least a temperaturepeak value, a speed peak value and a vibration peak value in response tothe plurality of detection values and analyze the peak values relativeto buffered detection values, specifications and anticipated stateinformation resulting in a conveyor performance parameter; and a peakresponse circuit structured to perform at least one operation inresponse to one of a peak value and a conveyor performance parameter.

An example system for estimating an agitator health parameter, thesystem includes: a data acquisition circuit structured to interpret aplurality of detection values from a plurality of input sensorscommunicatively coupled to the data acquisition circuit, each of theplurality of detection values corresponding to at least one of the inputsensors; a data storage circuit structured to store specifications,system geometry, and anticipated state information for an agitator andagitator components associated with the detection values, storehistorical agitator performance and buffer the plurality of detectionvalues for a predetermined length of time; a peak detection circuitstructured to determine a plurality of peak values comprising at least atemperature peak value, a speed peak value and a vibration peak value inresponse to the plurality of detection values and analyze the peakvalues relative to buffered detection values, specifications andanticipated state information resulting in an agitator performanceparameter; and a peak response circuit structured to perform at leastone operation in response to one of a peak value and an agitatorperformance parameter. In certain embodiments, a system further includeswhere the agitator is one of a rotating tank mixer, a large tank mixer,a portable tank mixer, a tote tank mixer, a drum mixer, a mounted mixerand a propeller mixer.

An example system for estimating a compressor health parameter, thesystem includes: a data acquisition circuit structured to interpret aplurality of detection values from a plurality of input sensorscommunicatively coupled to the data acquisition circuit, each of theplurality of detection values corresponding to at least one of the inputsensors; a data storage circuit structured to store specifications,system geometry, and anticipated state information for a compressor andcompressor components associated with the detection values, storehistorical compressor performance and buffer the plurality of detectionvalues for a predetermined length of time; a peak detection circuitstructured to determine a plurality of peak values comprising at least atemperature peak value, a speed peak value and a vibration peak value inresponse to the plurality of detection values and analyze the peakvalues relative to buffered detection values, specifications andanticipated state information resulting in a compressor performanceparameter; and a peak response circuit structured to perform at leastone operation in response to one of a peak value and a compressorperformance parameter.

An example system for estimating an air conditioner health parameter,the system includes: a data acquisition circuit structured to interpreta plurality of detection values from a plurality of input sensorscommunicatively coupled to the data acquisition circuit, each of theplurality of detection values corresponding to at least one of the inputsensors; a data storage circuit structured to store specifications,system geometry, and anticipated state information for an airconditioner and air conditioner components associated with the detectionvalues, store historical air conditioner performance and buffer theplurality of detection values for a predetermined length of time; a peakdetection circuit structured to determine a plurality of peak valuescomprising at least a temperature peak value, a speed peak value, apressure value and a vibration peak value in response to the pluralityof detection values and analyze the peak values relative to buffereddetection values, specifications and anticipated state informationresulting in an air conditioner performance parameter; and a peakresponse circuit structured to perform at least one operation inresponse to one of a peak value and an air conditioner performanceparameter.

An example system for estimating a centrifuge health parameter, thesystem includes: a data acquisition circuit structured to interpret aplurality of detection values from a plurality of input sensorscommunicatively coupled to the data acquisition circuit, each of theplurality of detection values corresponding to at least one of the inputsensors; a data storage circuit structured to store specifications,system geometry, and anticipated state information for a centrifuge andcentrifuge components associated with the detection values, storehistorical centrifuge performance and buffer the plurality of detectionvalues for a predetermined length of time; a peak detection circuitstructured to determine a plurality of peak values comprising at least atemperature peak value, a speed peak value and a vibration peak value inresponse to the plurality of detection values and analyze the peakvalues relative to buffered detection values, specifications andanticipated state information resulting in a centrifuge performanceparameter; and a peak response circuit structured to perform at leastone operation in response to one of a peak value and a centrifugeperformance parameter.

Bearings are used throughout many different types of equipment andapplications. Bearings may be present in or supporting shafts, motors,rotors, stators, housings, frames, suspension systems and components,gears, gear sets of various types, other bearings, and other elements.Bearings may be used as support for high speed vehicles such as maglevtrains. Bearings are used to support rotating shafts for engines,motors, generators, fans, compressors, turbines and the like. Giantroller bearings may be used to support buildings and physicalinfrastructure. Different types of bearings may be used to supportconventional, planetary and other types of gears. Bearings may be usedto support transmissions and gear boxes such as roller thrust bearings,for example. Bearings may be used to support wheels, wheel hubs andother rolling parts using tapered roller bearings.

There are many different types of bearings such as roller bearings,needle bearings, sleeve bearings, ball bearings, radial bearings, thrustload bearings including ball thrust bearings used in low speedapplications and roller thrust bearings, taper bearings and taperedroller bearings, specialized bearings, magnetic bearings, giant rollerbearings, jewel bearings (e.g., Sapphire), fluid bearings, flexurebearings to support bending element loads, and the like. References tobearings throughout this disclosure is intended to include, but not belimited by, the terms listed above.

In embodiments, information about the health or other status or stateinformation of or regarding a bearing in a piece of industrial equipmentor in an industrial process may be obtained by monitoring the conditionof various components of the industrial equipment or industrial process.Monitoring may include monitoring the amplitude and/or frequency and/orphase of a sensor signal measuring attributes such as temperature,humidity, acceleration, displacement and the like.

An embodiment of a data monitoring device 9200 is shown in FIG. 85 andmay include a plurality of sensors 9206 communicatively coupled to acontroller 9202. The controller 9202 may include a data acquisitioncircuit 9204, a data storage circuit 9216, a signal evaluation circuit9208 and, optionally, a response circuit 9210. The signal evaluationcircuit 9208 may comprise a frequency transformation circuit 9212 and afrequency evaluation circuit 9214.

The plurality of sensors 9206 may be wired to ports 9226 (reference FIG.86) on the data acquisition circuit 9204. The plurality of sensors 9206may be wirelessly connected to the data acquisition circuit 9204. Thedata acquisition circuit 9204 may be able to access detection valuescorresponding to the output of at least one of the plurality of sensors9206 where the sensors 9206 may be capturing data on differentoperational aspects of a bearing or piece of equipment orinfrastructure.

The selection of the plurality of sensors 9206 for the data monitoringdevice 9200 designed for a specific bearing or piece of equipment maydepend on a variety of considerations such as accessibility forinstalling new sensors, incorporation of sensors in the initial design,anticipated operational and failure conditions, reliability of thesensors, and the like. The impact of failure may drive the extent towhich a bearing or piece of equipment is monitored with more sensorsand/or higher capability sensors being dedicated to systems whereunexpected or undetected bearing failure would be costly or have severeconsequences.

The signal evaluation circuit 9208 may process the detection values toobtain information about a bearing being monitored. The frequencytransformation circuit 9212 may transform one or more time-baseddetection values to frequency information. The transformation may beaccomplished using techniques such as a digital Fast Fourier transform(“FFT”), Laplace transform, Z-transform, wavelet transform, otherfrequency domain transform, or other digital or analog signal analysistechniques, including, without limitation, complex analysis, includingcomplex phase evolution analysis.

The frequency evaluation circuit 9214 (or frequency analysis circuit)may be structured to detect signals at frequencies of interest.Frequencies of interest may include frequencies higher than thefrequency at which the equipment rotates (as measured by a tachometer,for instance), various harmonics and/or resonant frequencies associatedwith the equipment design and operating conditions such as multiples ofshaft rotation velocities or other rotating components for the equipmentthat is borne by the bearings. Changes in energy at frequencies close tothe operating frequency may be an indicator of balance/imbalance in thesystem. Changes in energy at frequencies on the order of twice theoperating frequency may be indicative of a system misalignment—forexample, on the coupling, or a looseness in the system, (e.g., rattlingat harmonics of the operating frequency). Changes in energy atfrequencies close to three or four times the operating frequency,corresponding to the number of bolts on a coupling, may indicate wear ofon one of the couplings. Changes in energy at frequencies of four, five,or more times the operating frequency may relate back to something thathas a corresponding number of elements, such as if there are energypeaks or activity around five times the operating frequency there may bewear or an imbalance in a five-vane pump or the like.

In an illustrative and non-limiting example, in the analysis of rollerbearings, frequencies of interest may include ball spin frequencies,cage spin frequencies, inner race frequency (as bearings often sit on arace inside a cage), outer race frequency and the like. Bearings thatare damaged or beginning to fail may show humps of energy at thefrequencies mentioned above and elsewhere in this disclosure. The energyat these frequencies may increase over time as the bearings wear moreand become more damaged due to more variations in rotationalacceleration and pings.

In an illustrative and non-limiting example, bad bearings may show humpsof energy and the intensity of high frequency measurements may start togrow over time as bearings wear and become imperfect (greateracceleration and pings may show up in high frequency measurementdomains). Those measurements may be indicators of air gaps in thebearing system. As bearings begin to wear, harder hits may cause theenergy signal to move to higher frequencies.

In embodiments, the signal evaluation circuit 9208 may also include oneor more of a phase detection circuit, a phase lock loop circuit, abandpass filter circuit, a peak detection circuit, and the like.

In embodiments, the signal evaluation circuit 9208 may include atransitory signal analysis circuit. Transient signals may cause smallamplitude vibrations. However, the challenge in bearing analysis is thatyou may receive a signal associated with a single or non-periodic impactand an exponential decay. Thus, the oscillation of the bearing may notbe represented by a single sine wave, but rather by a spectrum of manyhigh frequency sine waves. For example, a signal from a failing bearingmay only be seen, in a time-based signal, as a low amplitude spike for ashort amount of time. A signal from a failing bearing may be lower inamplitude than a signal associated with an imbalance even though theconsequences of a failed bearing may be more significant. It isimportant to be able to identify these signals. This type of lowamplitude, transient signal may be best analyzed using transientanalysis rather than a conventional frequency transformation, such as anFFT, which would treat the signal like a low frequency sine wave. Ahigher resolution data stream may also provide additional data for thedetection of transitory signals in low speed operations. Theidentification of transitory signals may enable the identification ofdefects in a piece of equipment or component operating at low RPMs.

In embodiments, the transitory signal analysis circuit for bearinganalysis may include envelope modulation analysis and other transitorysignal analysis techniques. The signal evaluation circuit 9208 may storelong stream of detection values to the data storage circuit 9216. Thetransitory signal analysis circuit may use envelope analysis techniqueson those long streams of detection values to identify transient effects(such as impacts) which may not be identified by conventional sine waveanalysis (such as FFTs).

The signal evaluation circuit 9208 may utilize transitory signalanalysis models optimized for the type of component being measured suchas bearings, gears, variable speed machinery and the like. In anillustrative and non-limiting example, a gear may resonate close to itsaverage rotational speed. In an illustrative and non-limiting example, abearing may resonate close to the bearing rotation frequency and producea ringing in amplitude around that frequency. For example, if the shaftinner race is wearing there may be chatter between the inner race andthe shaft resulting in amplitude modulation to the left and right of thebearing frequency. The amplitude modulation may demonstrate its own sinewave characteristics with its own side bands. Various signal processingtechniques may be used to eliminate the sinusoidal component, resultingin a modulation envelope for analysis.

The signal evaluation circuit 9208 may be optimized for variable speedmachinery. Historically, variable speed machinery was expensive to make,and it was common to use DC motors and variable sheaves, such that flowcould be controlled using vanes. Variable speed motors became morecommon with solid-state drive advances (“SCR devices”). The baseoperating frequency of equipment may be varied from the 50-60 Hzprovided by standard utility companies and either and slowed down orsped up to run the equipment at different speeds depending on theapplication. The ability to run the equipment at varying speeds mayresult in energy savings. However, depending on the equipment geometry,there may be some speeds which create vibrations at resonantfrequencies, reducing the life of the components. Variable speed motorsmay also emit electricity into bearings which may damage the bearings.In embodiments, the analysis of long data streams for envelopemodulation analysis and other transitory signal analysis techniques asdescribed herein may be useful in identifying these frequencies suchthat control schemes for the equipment may be designed to avoid thosespeeds which result in unacceptable vibrations and/or damage to thebearings.

In an illustrative and non-limiting example, heating, ventilation andair conditioning (“HVAC”) systems may be assembled on site usingvariable speed motors, fans, belts, compressors and the like where theoperating speeds are not constant, and their relative relationships areunknown. In an illustrative and non-limiting example, variable speedmotors may be used in fan pumps for building air circulation. Variablespeed motors may be used to vary the speed of conveyors—for example, inmanufacturing assembly lines or steel mills. Variable speed motors maybe used for fans in a pharmaceutical process, such as where it may becritical to avoid vibration.

In an illustrative and non-limiting example, sleeve bearings may beanalyzed for defects. Sleeve bearings typically have an oil system. Ifthe oil flow stops or the oil becomes severely contaminated, failure canoccur very quickly. Therefore, a fluid particulate sensor or fluidpressure sensors may be an important source of detection values.

In an illustrative and non-limiting example, fan integrity may beevaluated by measuring air pulsations related to blade pass frequencies.For example, if a fan has 12 blades, 12 air pulsations may be measured.Variations in the amplitude of the pulsations associated with thedifferent blades may be indicative of changes in a fan blade. Changes infrequencies associated with the air pulsations may be indicative ofbearing problems.

In an illustrative and non-limiting example, compressors used in the gasand oil field or in gas handling equipment on an assembly line may beevaluated by measuring the periodic increases in energy/pressure in thestorage vessel as gas is pumped into the vessel. Periodic variations inthe amplitude of the energy increases may be associated with piston wearor damage to a portion of a rotary screw. Phase evaluation of the energysignal relative to timing signals may be helpful in identifying whichpiston or portion of the rotary screw has damage. Changes in frequenciesassociated with the energy pulsations may be indicative of bearingproblems.

In an illustrative and non-limiting example, cavitation/air pockets inpumps may create shuttering in the pump housing and the output flowwhich may be identified with the frequency transformation and frequencyanalysis techniques described above and elsewhere herein.

In an illustrative and non-limiting example, the frequencytransformation and frequency analysis techniques described above andelsewhere herein may assist in the identification of problems incomponents of building HVAC systems such as big fans. If the dampers ofthe system are set poorly it may result in ducts pulsing or vibrating asair is pushed through the system. Monitoring of vibration sensors on theducts may assist in the balancing of the system. If there are defects inthe blades of the big fan this may also result in uneven air flow andresulting pulsation in the buildings ductwork.

In an illustrative and non-limiting example, detection values fromacoustical sensors located close to the bearings may assist in theidentification of issues in the engagement between gears or badbearings. Based on a knowledge of gear ratios, such as the “in” and“out” gear ratios, for a system and measurements of the input and outputrotational speed, detection values may be evaluated for energy occurringat those ratios, which in turn may be used to identify bad bearings.This could be done with simple off the shelf motors rather thanrequiring extensive retrofitting of the motor with sensors.

Based on the output of its various components, the signal evaluationcircuit 9208 may make a bearing life prediction, identify a bearinghealth parameter, identify a bearing performance parameter, determine abearing health parameter (e.g., fault conditions), and the like. Thesignal evaluation circuit 9208 may identify wear on a bearing, identifythe presence of foreign matter (e.g., particulates) in the bearings,identify air gaps or a loss of fluid in oil/fluid coated bearings,identify a loss of lubrication in a set of bearings, identify a loss ofpower for magnetic bearings and the like, identify strain/stress offlexure bearings, and the like. The signal evaluation circuit 9208 mayidentify optimal operation parameters for a piece of equipment to extendbearing life. The signal evaluation circuit 9208 may identify behavior(resonant wobble) at a selected operational frequency (e.g., shaftrotation rate).

The signal evaluation circuit 9208 may communicate with the data storagecircuit 9216 to access equipment specifications, equipment geometry,bearing specifications, bearing materials, anticipated state informationfor a plurality of bearing types, operational history, historicaldetection values, and the like for use in assessing the output of itsvarious components. The signal evaluation circuit 9208 may buffer asubset of the plurality of detection values, intermediate data such astime-based detection values transformed to frequency information,filtered detection values, identified frequencies of interest, and thelike for a predetermined length of time. The signal evaluation circuit9208 may periodically store certain detection values in the data storagecircuit 9216 to enable the tracking of component performance over time.In embodiments, based on relevant operating conditions and/or failuremodes that may occur as detection values approach one or more criteria,the signal evaluation circuit 9208 may store data in the data storagecircuit 9216 based on the fit of data relative to one or more criteria,such as those described throughout this disclosure. Based on one sensorinput meeting or approaching specified criteria or range, the signalevaluation circuit 9208 may store additional data such as RPMs,component loads, temperatures, pressures, vibrations or other sensordata of the types described throughout this disclosure in the datastorage circuit 9216. The signal evaluation circuit 9208 may store dataat a higher data rate for greater granularity in future processing, theability to reprocess at different sampling rates, and/or to enablediagnosing or post-processing of system information where operationaldata of interest is flagged, and the like.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, thesensors 9206 may comprise one or more of, without limitation, avibration sensor, an optical vibration sensor, a thermometer, ahygrometer, a voltage sensor, a current sensor, an accelerometer, avelocity detector, a light or electromagnetic sensor (e.g., determiningtemperature, composition and/or spectral analysis, and/or objectposition or movement), an image sensor, a structured light sensor, alaser-based image sensor, an infrared sensor, an acoustic wave sensor, aheat flux sensor, a displacement sensor, a turbidity meter, a viscositymeter, a load sensor, a tri-axial vibration sensor, an accelerometer, atachometer, a fluid pressure meter, an air flow meter, a horsepowermeter, a flow rate meter, a fluid particle detector, an acousticalsensor, a pH sensor, and the like, including, without limitation, any ofthe sensors described throughout this disclosure and the documentsincorporated by reference. The sensors may typically comprise at least atemperature sensor, a load sensor, a tri-axial sensor and a tachometer.

The sensors 9206 may provide a stream of data over time that has a phasecomponent, such as relating to acceleration or vibration, allowing forthe evaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors9206 may provide a stream of data that is not conventionallyphase-based, such as temperature, humidity, load, and the like. Thesensors 9206 may provide a continuous or near continuous stream of dataover time, periodic readings, event-driven readings, and/or readingsaccording to a selected interval or schedule.

In embodiments, as illustrated in FIG. 85, the sensors 9206 may be partof the data monitoring device 9200, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 86 and 87, oneor more external sensors 9224, which are not explicitly part of amonitoring device 9218 but rather are new, previously attached to orintegrated into the equipment or component, may be opportunisticallyconnected to or accessed by the monitoring device 9218. The monitoringdevice 9218 may include a controller 9220. The controller 9202 mayinclude a data acquisition circuit 9222, the data storage circuit 9216,the signal evaluation circuit 9208 and, optionally, the response circuit9210. The signal evaluation circuit 9208 may comprise the frequencytransformation circuit 9212 and the frequency analysis circuit 9214. Thedata acquisition circuit 9222 may include one or more of the input ports9226. The one or more of the external sensors 9224 may be directlyconnected to the one or more of the input ports 9226 on the dataacquisition circuit 9222 of the controller 9220 or may be accessed bythe data acquisition circuit 9222 wirelessly, such as by a reader,interrogator, or other wireless connection, such as over ashort-distance wireless protocol. In embodiments as shown in FIG. 87,the data acquisition circuit 9222 may further comprise a wirelesscommunications circuit 9262. The data acquisition circuit 9222 may usethe wireless communications circuit 9262 to access detection valuescorresponding to the one or more of the external sensors 9224 wirelesslyor via a separate source or some combination of these methods.

In embodiments, as illustrated in FIG. 88, the data acquisition circuit9222 may further comprise a multiplexer circuit 9236 as describedelsewhere herein. Outputs from the multiplexer circuit 9236 may beutilized by the signal evaluation circuit 9208. The response circuit9210 may have the ability to turn on and off portions of the multiplexorcircuit 9236. The response circuit 9210 may have the ability to controlthe control channels of the multiplexor circuit 9236.

The response circuit 9210 may initiate actions based on a bearingperformance parameter, a bearing health value, a bearing life predictionparameter, and the like. The response circuit 9210 may evaluate theresults of the signal evaluation circuit 9208 and, based on certaincriteria or the output from various components of the signal evaluationcircuit 9208, initiate an action. The criteria may include a sensor'sdetection values at certain frequencies or phases relative to a timersignal where the frequencies or phases of interest may be based on theequipment geometry, equipment control schemes, system input, historicaldata, current operating conditions, and/or an anticipated response. Thecriteria may include a sensor's detection values at certain frequenciesor phases relative to detection values of a second sensor. The criteriamay include signal strength at certain resonant frequencies/harmonicsrelative to detection values associated with a system tachometer oranticipated based on equipment geometry and operation conditions.Criteria may include a predetermined peak value for a detection valuefrom a specific sensor, a cumulative value of a sensor's correspondingdetection value over time, a change in peak value, a rate of change in apeak value, and/or an accumulated value (e.g., a time spent above/belowa threshold value, a weighted time spent above/below one or morethreshold values, and/or an area of the detected value above/below oneor more threshold values). The criteria may comprise combinations ofdata from different sensors such as relative values, relative changes invalue, relative rates of change in value, relative values over time, andthe like. The relative criteria may change with other data orinformation such as process stage, type of product being processed, typeof equipment, ambient temperature and humidity, external vibrations fromother equipment, and the like. The relative criteria may be reflected inone or more calculated statistics or metrics (including ones generatedby further calculations on multiple criteria or statistics), which inturn may be used for processing (such as on-board a data collector or byan external system), such as to be provided as an input to one or moreof the machine learning capabilities described in this disclosure, to acontrol system (which may be on board a data collector or remote, suchas to control selection of data inputs, multiplexing of sensor data,storage, or the like), or as a data element that is an input to anothersystem, such as a data stream or data package that may be available to adata marketplace, a SCADA system, a remote control system, a maintenancesystem, an analytic system, or other system.

Certain embodiments are described herein as detected values exceedingthresholds or predetermined values, but detected values may also fallbelow thresholds or predetermined values—for example, where an amount ofchange in the detected value is expected to occur, but detected valuesindicate that the change may not have occurred. For example, and withoutlimitation, vibrational data may indicate system agitation levels,properly operating equipment, or the like, and vibrational data belowamplitude and/or frequency thresholds may be an indication of a processthat is not operating according to expectations. Except where thecontext clearly indicates otherwise, any description herein describing adetermination of a value above a threshold and/or exceeding apredetermined or expected value is understood to include determinationof a value below a threshold and/or falling below a predetermined orexpected value.

The predetermined acceptable range may be based on anticipated systemresponse or vibration based on the equipment geometry and control schemesuch as number of bearings, relative rotational speed, influx of powerto the system at a certain frequency, and the like. The predeterminedacceptable range may also be based on long term analysis of detectionvalues across a plurality of similar equipment and components andcorrelation of data with equipment failure.

In some embodiments, an alert may be issued based on some of thecriteria discussed above. In an illustrative example, an increase intemperature and energy at certain frequencies may indicate a hot bearingthat is starting to fail. In embodiments, the relative criteria for analarm may change with other data or information such as process stage,type of product being processed on equipment, ambient temperature andhumidity, external vibrations from other equipment and the like. In anillustrative and non-limiting example, the response circuit 9210 mayinitiate an alert if a vibrational amplitude and/or frequency exceeds apredetermined maximum value, if there is a change or rate of change thatexceeds a predetermined acceptable range, and/or if an accumulated valuebased on vibrational amplitude and/or frequency exceeds a threshold.

In embodiments, response circuit 9210 may cause the data acquisitioncircuit 9204 to enable or disable the processing of detection valuescorresponding to certain sensors based on some of the criteria discussedabove. This may include switching to sensors having different responserates, sensitivity, ranges, and the like, or accessing new sensors ortypes of sensors, and the like. Switching may be undertaken based on amodel, a set of rules, or the like. In embodiments, switching may beunder control of a machine learning system, such that switching iscontrolled based on one or more metrics of success, combined with inputdata, over a set of trials, which may occur under supervision of a humansupervisor or under control of an automated system. Switching mayinvolve switching from one input port to another (such as to switch fromone sensor to another). Switching may involve altering the multiplexingof data, such as combining different streams under differentcircumstances. Switching may also involve activating a system to obtainadditional data, such as moving a mobile system (such as a robotic ordrone system), to a location where different or additional data isavailable (such as positioning an image sensor for a different view orpositioning a sonar sensor for a different direction of collection) orto a location where different sensors can be accessed (such as moving acollector to connect up to a sensor that is disposed at a location in anenvironment by a wired or wireless connection). This switching may beimplemented by changing the control signals for the multiplexor circuit9236 and/or by turning on or off certain input sections of themultiplexor circuit 9236. The response circuit 9210 may makerecommendations for the replacement of certain sensors in the futurewith sensors having different response rates, sensitivity, ranges, andthe like. The response circuit 9210 may recommend design alterations forfuture embodiments of the component, the piece of equipment, theoperating conditions, the process, and the like.

In embodiments, the response circuit 9210 may recommend maintenance atan upcoming process stop or initiate a maintenance call. The responsecircuit 9210 may recommend changes in process or operating parameters toremotely balance the piece of equipment. In embodiments, the responsecircuit 9210 may implement or recommend process changes—for example tolower the utilization of a component that is near a maintenanceinterval, operating off-nominally, or failed for purpose but still atleast partially operational, to change the operating speed of acomponent (such as to put it in a lower-demand mode), to initiateamelioration of an issue (such as to signal for additional lubricationof a roller bearing set, or to signal for an alignment process for asystem that is out of balance), and the like.

In embodiments as shown in FIGS. 89, 90, 91, and 92, a data monitoringsystem 9240 may include at least one of a data monitoring device 9250.The at least one of the data monitoring device 9250 may include thesensors 9206 and a controller 9242 comprising the data acquisitioncircuit 9204, the signal evaluation circuit 9208, the data storagecircuit 9216, and a communications circuit 9246. The signal evaluationcircuit 9208 may include at least one of the frequency detection circuit9212 and the frequency analysis circuit 9214. There may also be anoptional response circuit as described above and elsewhere herein. Thesignal evaluation circuit 9208 may periodically share data with thecommunication circuit 9246 for transmittal to a remote server 9244 toenable the tracking of component and equipment performance over time andunder varying conditions by a monitoring application 9248. Becauserelevant operating conditions and/or failure modes may occur as sensorvalues approach one or more criteria, the signal evaluation circuit 9208may share data with the communication circuit 9246 for transmittal tothe remote server 9244 based on the fit of data relative to one or morecriteria. Based on one sensor input meeting or approaching specifiedcriteria or range, the signal evaluation circuit 9208 may shareadditional data such as RPMs, component loads, temperatures, pressures,vibrations, and the like for transmittal. The signal evaluation circuit9208 may share data at a higher data rate for transmittal to enablegreater granularity in processing on the remote server.

In embodiments, as shown in FIG. 89, the communications circuit 9246 maycommunicate data directly to the remote server 9244. In embodiments, asshown in FIG. 90, the communications circuit 9246 may communicate datato an intermediate computer 9252, which may include a processor 9254running an operating system 9256 and a data storage circuit 9258. Theintermediate computer 9252 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server9244.

In embodiments, as illustrated in FIGS. 91 and 92, a data collectionsystem 9260 may have a plurality of the monitoring devices 9250collecting data on multiple components in a single piece of equipment,collecting data on the same component across a plurality of pieces ofequipment, (both the same and different types of equipment) in the samefacility as well as collecting data from monitoring devices in multiplefacilities. The monitoring application 9248 on the remote server 9244may receive and store one or more of the following: detection values,timing signals and data coming from a plurality of the variousmonitoring devices 9250. In embodiments, as shown in FIG. 91, thecommunications circuit 9246 may communicate data directly to the remoteserver 9244. In embodiments, as shown in FIG. 92, the communicationscircuit 9246 may communicate data to the intermediate computer 9252,which may include the processor 9254 running the operating system 9256and the data storage circuit 9258. There may be the individualintermediate computer 9252 associated with each of a monitoring device9264 or the individual intermediate computer 9252 may be associated witha plurality of the monitoring devices 9250 where the intermediatecomputer 9252 may collect data from a plurality of data monitoringdevices and send the cumulative data to the remote server 9244.

The monitoring application 9248 may select subsets of the detectionvalues, timing signals and data to be jointly analyzed. Subsets foranalysis may be selected based on a bearing type, bearing materials, ora single type of equipment in which a bearing is operating. Subsets foranalysis may be selected or grouped based on common operating conditionsor operational history such as size of load, operational condition(e.g., intermittent, continuous), operating speed or tachometer, commonambient environmental conditions such as humidity, temperature, air orfluid particulate, and the like. Subsets for analysis may be selectedbased on common anticipated state information. Subsets for analysis maybe selected based on the effects of other nearby equipment such asnearby machines rotating at similar frequencies, nearby equipmentproducing electromagnetic fields, nearby equipment producing heat,nearby equipment inducing movement or vibration, nearby equipmentemitting vapors, chemicals or particulates, or other potentiallyinterfering or intervening effects.

The monitoring application 9248 may analyze a selected subset. In anillustrative example, data from a single component may be analyzed overdifferent time periods, such as one operating cycle, cycle-to-cyclecomparisons, trends over several operating cycles/times such as a month,a year, the life of the component, or the like. Data from multiplecomponents of the same type may also be analyzed over different timeperiods. Trends in the data such as changes in frequency or amplitudemay be correlated with failure and maintenance records associated withthe same component or piece of equipment. Trends in the data such aschanging rates of change associated with start-up or different points inthe process may be identified. Additional data may be introduced intothe analysis such as output product quality, output quantity (such asper unit of time), indicated success or failure of a process, and thelike. Correlation of trends and values for different types of data maybe analyzed to identify those parameters whose short-term analysis mightprovide the best prediction regarding expected performance. The analysismay identify model improvements to the model for anticipated stateinformation, recommendations around sensors to be used, positioning ofsensors and the like. The analysis may identify additional data tocollect and store. The analysis may identify recommendations regardingneeded maintenance and repair and/or the scheduling of preventativemaintenance. The analysis may identify recommendations around purchasingreplacement bearings and the timing of the replacement of the bearings.The analysis may result in warning regarding the dangers of catastrophicfailure conditions. This information may be transmitted back to themonitoring device to update types of data collected and analyzed locallyor to influence the design of future monitoring devices.

In embodiments, the monitoring application 9248 may have access toequipment specifications, equipment geometry, bearing specifications,bearing materials, anticipated state information for a plurality ofbearing types, operational history, historical detection values, bearinglife models and the like for use analyzing the selected subset usingrule-based or model-based analysis. In embodiments, the monitoringapplication 9248 may feed a neural net with the selected subset to learnto recognize various operating state, health states (e.g., lifetimepredictions) and fault states utilizing deep learning techniques. Inembodiments, a hybrid of the two techniques (model-based learning anddeep learning) may be used.

In an illustrative and non-limiting example, the health of bearings onconveyors and lifters in an assembly line, in water pumps on industrialvehicles and in compressors in gas handling systems, in compressorssituated out in the gas and oil fields, in factory air conditioningunits and in factory mineral pumps may be monitored using the frequencytransformation and frequency analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of one or moreof bearings, gears, blades, screws and associated shafts, motors,rotors, stators, gears, and other components of gear boxes, motors,pumps, vibrating conveyors, mixers, centrifuges, drilling machines,screw drivers and refining tanks situated in the oil and gas fields maybe evaluated using the frequency transformation and frequency analysistechniques, data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of bearings andassociated shafts, motors, rotors, stators, gears, and other componentsof rotating tank/mixer agitators, mechanical/rotating agitators, andpropeller agitators, to promote chemical reactions deployed in chemicaland pharmaceutical production lines may be evaluated using the frequencytransformation and frequency analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of bearings andassociated shafts, motors, rotors, stators, gears, and other componentsof vehicle systems such as steering mechanisms or engines may beevaluated using the frequency transformation and frequency analysistechniques, data monitoring devices and data collection systemsdescribed herein.

An example monitoring device for bearing analysis in an industrialenvironment, includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage for storing specifications and anticipated state information fora plurality of bearing types and buffering the plurality of detectionvalues for a predetermined length of time; and a bearing analysiscircuit structured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a bearingperformance parameter.

In certain further embodiments, an example monitoring device includesone or more of: a response circuit to perform at least one operation inresponse to the bearing performance parameter. In embodiments, theplurality of input sensors includes at least two sensors selected fromthe group consisting of a temperature sensor, a load sensor, an opticalvibration sensor, an acoustic wave sensor, a heat flux sensor, aninfrared sensor, an accelerometer, a tri-axial vibration sensor and atachometer. In embodiments, the at least one operation is further inresponse to at least one of: a change in amplitude of at least one ofthe plurality of detection values; a change in frequency or relativephase of at least one of the plurality of detection values; a rate ofchange in both amplitude and relative phase of at least one of theplurality of detection values; and a relative rate of change inamplitude and relative phase of at least one of the plurality ofdetection values. In embodiments, the at least one operation comprisesissuing an alert. In embodiments, the alert may be one of haptic,audible and visual. In embodiments, the at least one operation furthercomprises storing additional data in the data storage circuit. Inembodiments, the storing additional data in the data storage circuit isfurther in response to at least one of: a change in the relative phasedifference and a relative rate of change in the relative phasedifference.

An example monitoring device for bearing analysis in an industrialenvironment, the monitoring device includes: a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a data storage for storing specifications andanticipated state information for a plurality of bearing types andbuffering the plurality of detection values for a predetermined lengthof time; and a bearing analysis circuit structured to analyze buffereddetection values relative to specifications and anticipated stateinformation resulting in a bearing health value.

In certain embodiments, an example monitoring device further includesone or more of: a response circuit to perform at least one operation inresponse to the bearing health value. In embodiments, the plurality ofinput sensors includes at least two sensors selected from the groupconsisting of a temperature sensor, a load sensor, an optical vibrationsensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor,an accelerometer, a tri-axial vibration sensor and a tachometer. Inembodiments, the at least one operation is further in response to atleast one of: a change in amplitude of at least one of the plurality ofdetection values; a change in frequency or relative phase of at leastone of the plurality of detection values; a rate of change in bothamplitude and relative phase of at least one of the plurality ofdetection values; and a relative rate of change in amplitude andrelative phase of at least one of the plurality of detection values. Inembodiments, the at least one operation comprises issuing an alert. Inembodiments, the alert may be one of haptic, audible and visual. Inembodiments, the at least one operation further comprises storingadditional data in the data storage circuit. In embodiments, the storingadditional data in the data storage circuit is further in response to atleast one of: a change in the relative phase difference and a relativerate of change in the relative phase difference.

An example monitoring device for bearing analysis in an industrialenvironment, includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage for storing specifications and anticipated state information fora plurality of bearing types and buffering the plurality of detectionvalues for a predetermined length of time; and a bearing analysiscircuit structured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a bearinglife prediction parameter.

In certain embodiments, a monitoring device further includes one or moreof: a response circuit to perform at least one operation in response tothe bearing life prediction parameter. In embodiments, the plurality ofinput sensors includes at least two sensors selected from the groupconsisting of a temperature sensor, a load sensor, an optical vibrationsensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor,an accelerometer, a tri-axial vibration sensor and a tachometer. Inembodiments, the at least one operation is further in response to atleast one of: a change in amplitude of at least one of the plurality ofdetection values; a change in frequency or relative phase of at leastone of the plurality of detection values; a rate of change in bothamplitude and relative phase of at least one of the plurality ofdetection values; and a relative rate of change in amplitude andrelative phase of at least one of the plurality of detection values. Inembodiments, the at least one operation comprises issuing an alert. Inembodiments, the alert may be one of haptic, audible and visual. Inembodiments, the at least one operation further comprises storingadditional data in the data storage circuit. In embodiments, the storingadditional data in the data storage circuit is further in response to atleast one of: a change in the relative phase difference and a relativerate of change in the relative phase difference.

An example monitoring device for bearing analysis in an industrialenvironment, includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage for storing specifications and anticipated state information fora plurality of bearing types and buffering the plurality of detectionvalues for a predetermined length of time; and a bearing analysiscircuit structured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a bearingperformance parameter. In embodiments, the data acquisition circuitcomprises a multiplexer circuit whereby alternative combinations of thedetection values may be selected based on at least one of user input, adetected state and a selected operating parameter for a machine.

In certain further embodiments, an example monitoring device furtherincludes one or more of: a response circuit to perform at least oneoperation in response to the bearing performance parameter. Inembodiments, the plurality of input sensors includes at least twosensors selected from the group consisting of a temperature sensor, aload sensor, an optical vibration sensor, an acoustic wave sensor, aheat flux sensor, an infrared sensor, an accelerometer, a tri-axialvibration sensor and a tachometer; a change in amplitude of at least oneof the plurality of detection values; a change in frequency or relativephase of at least one of the plurality of detection values; a rate ofchange in both amplitude and relative phase of at least one of theplurality of detection values; and a relative rate of change inamplitude and relative phase of at least one of the plurality ofdetection values. In embodiments, the at least one operation comprisesissuing an alert. In embodiments, the alert may be one of haptic,audible and visual. In embodiments, the at least one operation furthercomprises storing additional data in the data storage circuit. Inembodiments, the storing additional data in the data storage circuit isfurther in response to at least one of: a change in the relative phasedifference and a relative rate of change in the relative phasedifference. In embodiments, the at least one operation comprisesenabling or disabling one or more portions of the multiplexer circuit,or altering the multiplexer control lines. In embodiments, the dataacquisition circuit comprises at least two multiplexer circuits and theat least one operation comprises changing connections between the atleast two multiplexer circuits.

An example system for data collection, processing, and bearing analysisin an industrial environment includes: a plurality of monitoringdevices, each monitoring device comprising: a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a data storage for storing specifications andanticipated state information for a plurality of bearing types andbuffering the plurality of detection values for a predetermined lengthof time; a bearing analysis circuit structured to analyze buffereddetection values relative to specifications and anticipated stateinformation resulting in a bearing life prediction; a communicationcircuit structured to communicate with a remote server providing thebearing life prediction and a portion of the buffered detection valuesto the remote server; and a monitoring application on the remote serverstructured to receive, store and jointly analyze a subset of thedetection values from the plurality of monitoring devices.

In certain further embodiments, an example monitoring device includesone or more of: a response circuit to perform at least one operation inresponse to the bearing life prediction. In embodiments, the pluralityof input sensors includes at least two sensors selected from the groupconsisting of a temperature sensor, a load sensor, an optical vibrationsensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor,an accelerometer, a tri-axial vibration sensor and a tachometer. Inembodiments, the at least one operation is further in response to atleast one of: a change in amplitude of at least one of the plurality ofdetection values; a change in frequency or relative phase of at leastone of the plurality of detection values; a rate of change in bothamplitude and relative phase of at least one of the plurality ofdetection values; and a relative rate of change in amplitude andrelative phase of at least one of the plurality of detection values. Inembodiments, the at least one operation comprises issuing an alert. Inembodiments, the alert may be one of haptic, audible and visual. Inembodiments, the at least one operation further comprises storingadditional data in the data storage circuit. In embodiments, the storingadditional data in the data storage circuit is further in response to atleast one of: a change in the relative phase difference and a relativerate of change in the relative phase difference.

An example system for data collection, processing, and bearing analysisin an industrial environment comprising: a plurality of monitoringdevices, each comprising: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage for storing specifications and anticipated state information fora plurality of bearing types and buffering the plurality of detectionvalues for a predetermined length of time; a bearing analysis circuitstructured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a bearingperformance parameter; a communication circuit structured to communicatewith a remote server providing the life prediction and a portion of thebuffered detection values to the remote server; and a monitoringapplication on the remote server structured to receive, store andjointly analyze a subset of the detection values from the plurality ofmonitoring devices.

In certain further embodiments, an example monitoring device furtherincludes one or more of: a response circuit to perform at least oneoperation in response to the bearing performance parameter. Inembodiments, the plurality of input sensors includes at least twosensors selected from the group consisting of a temperature sensor, aload sensor, an optical vibration sensor, an acoustic wave sensor, aheat flux sensor, an infrared sensor, an accelerometer, a tri-axialvibration sensor and a tachometer. In embodiments, the at least oneoperation is further in response to at least one of: a change inamplitude of at least one of the plurality of detection values; a changein frequency or relative phase of at least one of the plurality ofdetection values; a rate of change in both amplitude and relative phaseof at least one the plurality of detection values; and a relative rateof change in amplitude and relative phase of at least one the pluralityof detection values. In embodiments, the at least one operationcomprises issuing an alert. In embodiments, the alert may be one ofhaptic, audible and visual. In embodiments, the at least one operationfurther comprises storing additional data in the data storage circuit.In embodiments, storing additional data in the data storage circuit isfurther in response to at least one of: a change in the relative phasedifference and a relative rate of change in the relative phasedifference.

An example system for data collection, processing, and bearing analysisin an industrial environment includes: a plurality of monitoringdevices, each monitoring device comprising: a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a streaming circuit for streaming at least a subsetof the acquired detection values to a remote learning system; and aremote learning system including a bearing analysis circuit structuredto analyze the detection values relative to a machine-basedunderstanding of the state of the at least one bearing.

In certain further embodiments, an example system further includes oneor more of. In embodiments, the machine-based understanding is developedbased on a model of the bearing that determines a state of the at leastone bearing based at least in part on the relationship of the behaviorof the bearing to an operating frequency of a component of theindustrial machine. In embodiments, the state of the at least onebearing is at least one of an operating state, a health state, apredicted lifetime state and a fault state. In embodiments, themachine-based understanding is developed based by providing inputs to adeep learning machine. In embodiments, the inputs comprise a pluralityof streams of detection values for a plurality of bearings and aplurality of measured state values for the plurality of bearings. Inembodiments, the state of the at least one bearing is at least one of anoperating state, a health state, a predicted lifetime state and a faultstate.

An example method of analyzing bearings and sets of bearings, includes:receiving a plurality of detection values corresponding to data from atemperature sensor, a vibration sensor positioned near the bearing orset of bearings and a tachometer to measure rotation of a shaftassociated with the bearing or set of bearings; comparing the detectionvalues corresponding to the temperature sensor to a predeterminedmaximum level; filtering the detection values corresponding to thevibration sensor through a high pass filter where the filter is selectedto eliminate vibrations associated with detection values associated withthe tachometer; identifying rapid changes in at least one of atemperature peak and a vibration peak; identifying frequencies at whichspikes in the filtered detection values corresponding to the vibrationsensor occur and comparing frequencies and spikes in amplitude relativeto an anticipated state information and specification associated withthe bearing or set of bearings; and determining a bearing healthparameter.

An example device for monitoring roller bearings in an industrialenvironment, includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage circuit structured to store specifications and anticipated stateinformation for a plurality of types of roller bearings and bufferingthe plurality of detection values for a predetermined length of time; abearing analysis circuit structured to analyze buffered detection valuesrelative to specifications and anticipated state information resultingin a bearing performance parameter; and

a response circuit to perform at least one operation in response to thebearing performance prediction. In embodiments, the plurality of inputsensors includes at least two sensors selected from the group consistingof a temperature sensor, a load sensor, an optical vibration sensor, anacoustic wave sensor, a heat flux sensor, an infrared sensor, anaccelerometer, a tri-axial vibration sensor and a tachometer.

An example device for monitoring sleeve bearings in an industrialenvironment, includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage for storing sleeve bearing specifications and anticipated stateinformation for types of sleeve bearings and buffering the plurality ofdetection values for a predetermined length of time; a bearing analysiscircuit structured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a bearingperformance parameter; and

a response circuit to perform at least one operation in response to thebearing performance parameter. In embodiments, the plurality of inputsensors includes at least two sensors selected from the group consistingof a temperature sensor, a load sensor, an optical vibration sensor, anacoustic wave sensor, a heat flux sensor, an infrared sensor, anaccelerometer, a tri-axial vibration sensor and a tachometer.

An example system for monitoring pump bearings in an industrialenvironment, includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage for storing pump specifications, bearing specifications,anticipated state information for pump bearings and buffering theplurality of detection values for a predetermined length of time; abearing analysis circuit structured to analyze buffered detection valuesrelative to specifications and anticipated state information resultingin a bearing performance parameter; and

a response circuit to perform at least one operation in response to thebearing performance parameter. In embodiments, the plurality of inputsensors includes at least two sensors selected from the group consistingof a temperature sensor, a load sensor, an optical vibration sensor, anacoustic wave sensor, a heat flux sensor, an infrared sensor, anaccelerometer, a tri-axial vibration sensor and a tachometer.

An example system for collection, processing, and analyzing pumpbearings in an industrial environment includes: a plurality ofmonitoring devices, each comprising: a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a data storage for storing pump specifications,bearing specifications, anticipated state information for pump bearingsand buffering the plurality of detection values for a predeterminedlength of time; a bearing analysis circuit structured to analyzebuffered detection values relative to the pump and bearingspecifications and anticipated state information resulting in a bearingperformance parameter; a communication circuit structured to communicatewith a remote server providing the bearing performance parameter and aportion of the buffered detection values to the remote server; and amonitoring application on the remote server structured to receive, storeand jointly analyze a subset of the detection values from the pluralityof monitoring devices.

An example system for estimating a conveyor health parameter, includes:a data acquisition circuit structured to interpret a plurality ofdetection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors. Inembodiments, the plurality of input sensors comprises at least one of anangular position sensor, an angular velocity sensor and an angularacceleration sensor positioned to measure the rotating component; a datastorage circuit structured to store specifications, system geometry, andanticipated state information for the conveyor and associated rotatingcomponents, store historical conveyor and component performance andbuffer the plurality of detection values for a predetermined length oftime; a bearing analysis circuit structured to analyze buffereddetection values relative to specifications and anticipated stateinformation resulting in a bearing performance parameter; and a systemanalysis circuit structured to utilize the bearing performance and atleast one of an anticipated state, historical data and a system geometryto estimate a conveyor health performance.

An example system for estimating an agitator health parameter, includes:a data acquisition circuit structured to interpret a plurality ofdetection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors. Inembodiments, the plurality of input sensors comprises at least one of anangular position sensor, an angular velocity sensor and an angularacceleration sensor positioned to measure the rotating component; a datastorage circuit structured to store specifications, system geometry, andanticipated state information for the agitator and associatedcomponents, store historical agitator and component performance andbuffer the plurality of detection values for a predetermined length oftime; a bearing analysis circuit structured to analyze buffereddetection values relative to specifications and anticipated stateinformation resulting in a bearing performance parameter; and a systemanalysis circuit structured to utilize the bearing performance and atleast one of an anticipated state, historical data and a system geometryto estimate an agitation health parameter. In certain furtherembodiments, an example device further includes where the agitator isone of a rotating tank mixer, a large tank mixer, a portable tank mixer,a tote tank mixer, a drum mixer, a mounted mixer and a propeller mixer.

An example system for estimating a vehicle steering system performanceparameter, includes: a data acquisition circuit structured to interpreta plurality of detection values, each of the plurality of detectionvalues corresponding to at least one of a plurality of input sensors. Inembodiments, the plurality of input sensors comprises at least one of anangular position sensor, an angular velocity sensor and an angularacceleration sensor positioned to measure the rotating component; a datastorage circuit structured to store specifications, system geometry, andanticipated state information for the vehicle steering system, the rack,the pinion, and the steering column, store historical steering systemperformance and buffer the plurality of detection values for apredetermined length of time; a bearing analysis circuit structured toanalyze buffered detection values relative to specifications andanticipated state information resulting in a bearing performanceparameter; and

a system analysis circuit structured to utilize the bearing performanceand at least one of an anticipated state, historical data and a systemgeometry to estimate a vehicle steering system performance parameter.

An example system for estimating a pump performance parameter, includes:a data acquisition circuit structured to interpret a plurality ofdetection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors. Inembodiments, the plurality of input sensors comprises at least one of anangular position sensor, an angular velocity sensor and an angularacceleration sensor positioned to measure the rotating component; a datastorage circuit structured to store specifications, system geometry, andanticipated state information for the pump and pump components, storehistorical steering system performance and buffer the plurality ofdetection values for a predetermined length of time; a bearing analysiscircuit structured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a bearingperformance parameter; a system analysis circuit structured to utilizethe bearing performance and at least one of an anticipated state,historical data and a system geometry to estimate a pump performanceparameter. In certain embodiments, and example system further includes.In embodiments, the pump is a water pump in a car, and/or. Inembodiments, the pump is a mineral pump.

An example system for estimating a performance parameter for a drillingmachine, includes: a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors. Inembodiments, the plurality of input sensors comprises at least one of anangular position sensor, an angular velocity sensor and an angularacceleration sensor positioned to measure the rotating component; a datastorage circuit structured to store specifications, system geometry, andanticipated state information for the drilling machine and drillingmachine components, store historical drilling machine performance andbuffer the plurality of detection values for a predetermined length oftime; a bearing analysis circuit structured to analyze buffereddetection values relative to specifications and anticipated stateinformation resulting in a bearing performance parameter; and

a system analysis circuit structured to utilize the bearing performanceand at least one of an anticipated state, historical data and a systemgeometry to estimate a performance parameter for the drilling machine.In certain further embodiments, the drilling machine is one of an oildrilling machine and a gas drilling machine.

An example system for estimating a performance parameter for a drillingmachine, includes: a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors. Inembodiments, the plurality of input sensors comprises at least one of anangular position sensor, an angular velocity sensor and an angularacceleration sensor positioned to measure the rotating component; a datastorage circuit structured to store specifications, system geometry, andanticipated state information for the drilling machine and drillingmachine components, store historical drilling machine performance andbuffer the plurality of detection values for a predetermined length oftime; a bearing analysis circuit structured to analyze buffereddetection values relative to specifications and anticipated stateinformation resulting in a bearing performance parameter; and a systemanalysis circuit structured to utilize bearing performance and at leastone of an anticipated state, historical data and a system geometry toestimate a performance parameter for the drilling machine.

Rotating components are used throughout many different types ofequipment and applications. Rotating components may include shafts,motors, rotors, stators, bearings, fins, vanes, wings, blades, fans,bearings, wheels, hubs, spokes, balls, rollers, pins, gears and thelike. In embodiments, information about the health or other status orstate information of or regarding a rotating component in a piece ofindustrial equipment or in an industrial process may be obtained bymonitoring the condition of the component or various other components ofthe industrial equipment or industrial process and identifying torsionon the component. Monitoring may include monitoring the amplitude andphase of a sensor signal, such as one measuring attributes such asangular position, angular velocity, angular acceleration, and the like.

An embodiment of a data monitoring device 9400 is shown in FIG. 93 andmay include a plurality of sensors 9406 communicatively coupled to acontroller 9402. The controller 9402 may include a data acquisitioncircuit 9404, a data storage circuit 9414, a system evaluation circuit9408 and, optionally, a response circuit 9410. The system evaluationcircuit 9408 may comprise a torsion analysis circuit 9412.

The plurality of the sensors 9406 may be wired to ports on the dataacquisition circuit 9404. The plurality of the sensors 9406 may bewirelessly connected to the data acquisition circuit 9404. The dataacquisition circuit 9404 may be able to access detection valuescorresponding to the output of at least one of the plurality of thesensors 9406 where the sensors 9406 may be capturing data on differentoperational aspects of a bearing or piece of equipment orinfrastructure.

The selection of the plurality of the sensors 9406 for the datamonitoring device 9400 designed to assess torsion on a component, suchas a shaft, motor, rotor, stator, bearing or gear, or other componentdescribed herein, or a combination of components, such as within orcomprising a drive train or piece of equipment or system, may depend ona variety of considerations such as accessibility for installing newsensors, incorporation of sensors in the initial design, anticipatedoperational and failure conditions, reliability of the sensors, and thelike. The impact of failure may drive the extent to which a bearing orpiece of equipment is monitored with more sensors and/or highercapability sensors being dedicated to systems where unexpected orundetected bearing failure would be costly or have severe consequences.To assess torsion the sensors may include, among other options, anangular position sensor and/or an angular velocity sensor and/or anangular acceleration sensor.

The system evaluation circuit 9408 may process the detection values toobtain information about one or more rotating components beingmonitored. The torsional analysis circuit 9412 may be structured toidentify torsion in a component or system, such as based on anticipatedstate, historical state, system geometry and the like, such as thatwhich is available from the data storage circuit 9414. The torsionalanalysis circuit 9412 may be structured to identify torsion using avariety of techniques such as amplitude, phase and frequency differencesin the detection values from two linear accelerometers positioned atdifferent locations on a shaft. The torsional analysis circuit 9412 mayidentify torsion using the difference in amplitude and phase between anangular accelerometer on a shaft and an angular accelerometer on a slipring on the end of the shaft. The torsional analysis circuit 9412 mayidentify shear stress/elongation on a component using two strain gaugesin a half bridge configuration or four strain gauges in a full bridgeconfiguration. The torsional analysis circuit 9412 may use coder basedtechniques such as markers to identify the rotation of a shaft, bearing,rotor, stator, gear or other rotating component. The markers beingassessed may include visual markers such as gear teeth or stripes on ashaft captured by an image sensor, light detector or the like. Themarkers being assessed may include magnetic components located on therotating component and sensed by an electromagnetic pickup. The sensormay be a Hall Effect sensor.

Additional input sensors may include a thermometer, a heat flux sensor,a magnetometer, an axial load sensor, a radial load sensor, anaccelerometer, a shear-stress torque sensor, a twist angle sensor andthe like. Twist angle may include rotational information at twopositions on shaft or an angular velocity or angular acceleration at twopositions on a shaft. In embodiments, the sensors may be positioned atdifferent ends of the shaft.

The torsional analysis circuit 9412 may include one or more of atransient signal analysis circuit and/or a frequency transformationcircuit and/or a frequency analysis circuit as described elsewhereherein.

In embodiments, the transitory signal analysis circuit for torsionalanalysis may include envelope modulation analysis, and other transitorysignal analysis techniques. The system evaluation circuit 9408 may storelong stream of detection values to the data storage circuit 9414. Thetransitory signal analysis circuit may use envelope analysis techniqueson those long streams of detection values to identify transient effects(such as impacts) which may not be identified by conventional sine waveanalysis (such as FFTs).

In embodiments, the frequencies of interest may include identifyingenergy at relation-order bandwidths for rotating equipment. The maximumorder observed may comprise a function of the bandwidth of the systemand the rotational speed of the component. For varying speeds (run-ups,run-downs, etc.), the minimum RPM may determine the maximum-observedorder. In embodiments, there may be torsional resonance at harmonics ofthe forcing frequency/frequency at which a component is being driven.

In an illustrative and non-limiting example, the monitoring device maybe used to collect and process sensor data to measure torsion on acomponent. The monitoring device may be in communication with or includea high resolution, high speed vibration sensor to collect data over anextended period of time, enough to measure multiple cycles of rotation.For gear driven equipment, the sampling resolution should be such thatthe number of samples taken per cycle is at least equal to the number ofgear teeth driving the component. It will be understood that a lowersampling resolution may also be utilized, which may result in a lowerconfidence determination and/or taking data over a longer period of timeto develop sufficient statistical confidence. This data may then be usedin the generation of a phase reference (relative probe) or tachometersignal for a piece of equipment. This phase reference may be used toalign phase data such as velocity and/or positional and/or accelerationdata from multiple sensors located at different positions on a componentor on different components within a system. This information mayfacilitate the determination of torsion for different components or thegeneration of an Operational Deflection Shape (“ODS”), indicating theextent of torsion on one or more components during an operational mode.

The higher resolution data stream may provide additional data for thedetection of transitory signals in low speed operations. Theidentification of transitory signals may enable the identification ofdefects in a piece of equipment or component.

In an illustrative and non-limiting example, the monitoring device maybe used to identify mechanical jitter for use in failure predictionmodels. The monitoring device may begin acquiring data when the piece ofequipment starts up through ramping up to operating speed or duringoperation. Once at operating speed, it is anticipated that the torsionaljitter should be minimal and changes in torsion during this phase may beindicative of cracks, bearing faults and the like. Additionally, knowntorsions may be removed from the signal to facilitate the identificationof unanticipated torsions resulting from system design flaws orcomponent wear. Having phase information associated with the datacollected at operating speed may facilitate identification of a locationof vibration and potential component wear. Relative phase informationfor a plurality of sensors located throughout a machine may facilitatethe evaluation of torsion as it is propagated through a piece ofequipment.

Based on the output of its various components, the system evaluationcircuit 9408 may make a component life prediction, identify a componenthealth parameter, identify a component performance parameter, and thelike. The system evaluation circuit 9408 may identify unexpected torsionon a rotating component, identify strain/stress of flexure bearings, andthe like. The system evaluation circuit 9408 may identify optimaloperation parameters for a piece of equipment to reduce torsion andextend component life. The system evaluation circuit 9408 may identifytorsion at selected operational frequencies (e.g., shaft rotationrates). Information about operational frequencies causing torsion mayfacilitate equipment operational balance in the future.

The system evaluation circuit 9408 may communicate with the data storagecircuit 9414 to access equipment specifications, equipment geometry,bearing specifications, component materials, anticipated stateinformation for a plurality of component types, operational history,historical detection values, and the like for use in assessing theoutput of its various components. The system evaluation circuit 9408 maybuffer a subset of the plurality of detection values, intermediate datasuch as time-based detection values, time-based detection valuestransformed to frequency information, filtered detection values,identified frequencies of interest, and the like for a predeterminedlength of time. The system evaluation circuit 9408 may periodicallystore certain detection values in the data storage circuit 9414 toenable the tracking of component performance over time. In embodiments,based on relevant operating conditions and/or failure modes, which mayoccur as detection values approach one or more criteria, the systemevaluation circuit 9408 may store data in the data storage circuit 9414based on the fit of data relative to one or more criteria, such as thosedescribed throughout this disclosure. Based on one sensor input meetingor approaching specified criteria or range, the system evaluationcircuit 9408 may store additional data such as RPM information,component loads, temperatures, pressures, vibrations or other sensordata of the types described throughout this disclosure in the datastorage circuit 9414. The system evaluation circuit 9408 may store datain the data storage circuit at a higher data rate for greatergranularity in future processing, the ability to reprocess at differentsampling rates, and/or to enable diagnosing or post-processing of systeminformation where operational data of interest is flagged, and the like.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, thesensors 9406 may comprise, without limitation, one or more of thefollowing: a displacement sensor, an angular velocity sensor, an angularaccelerometer, a vibration sensor, an optical vibration sensor, athermometer, a hygrometer, a voltage sensor, a current sensor, anaccelerometer, a velocity detector, a light or electromagnetic sensor(e.g., determining temperature, composition and/or spectral analysis,and/or object position or movement), an image sensor, a structured lightsensor, a laser-based image sensor, an infrared sensor, an acoustic wavesensor, a heat flux sensor, a displacement sensor, a turbidity meter, aviscosity meter, a load sensor, a tri-axial vibration sensor, anaccelerometer, a tachometer, a fluid pressure meter, an air flow meter,a horsepower meter, a flow rate meter, a fluid particle detector, anacoustical sensor, a pH sensor, and the like, including, withoutlimitation, any of the sensors described throughout this disclosure andthe documents incorporated by reference.

The sensors 9406 may provide a stream of data over time that has a phasecomponent, such as relating to angular velocity, angular acceleration orvibration, allowing for the evaluation of phase or frequency analysis ofdifferent operational aspects of a piece of equipment or an operatingcomponent. The sensors 9406 may provide a stream of data that is notconventionally phase-based, such as temperature, humidity, load, and thelike. The sensors 9406 may provide a continuous or near continuousstream of data over time, periodic readings, event-driven readings,and/or readings according to a selected interval or schedule.

When assessing engine components, in an illustrative and non-limitingexample, it may be desirable to remove vibrations due to the timing ofpiston vibrations or anticipated vibrational input due to crankshaftgeometry to assist in identifying other torsional forces on a component.This may assist in assessing the health of such diverse components as awater pump in a vehicle or positive displacement pumps.

In an illustrative and non-limiting example, torsional analysis and theidentification of variations in torsion may assist in the identificationof stick-slip in a gear or transfer system. In some cases, this may onlyoccur once per cycle, and phase information may be as important as ormore important than the amplitude of the signal in determining systemstate or behavior.

In an illustrative and non-limiting example, torsional analysis mayassist in the identification, prediction (e.g., timing) and evaluationof lash in a drive train and the follow-on torsion resulting from achange in direction or start up, which in turn may be used forcontrolling a system, assessing needs for maintenance, assessing needsfor balancing or otherwise re-setting components, or the like.

In an illustrative and non-limiting example, when assessing compressors,it may be desirable to remove vibrations due to the timing of pistonvibrations or anticipated vibrational input associated with thetechniques and geometry used for positive displacement compressors toassist in identifying other torsional forces on a component. This mayassist in assessing the health of compressors in such diverseenvironments as air conditioning units in factories, compressors in gashandling systems in an industrial environment, compressors in oilfields, and other environments as described elsewhere herein.

In an illustrative and non-limiting example, torsional analysis mayfacilitate the understanding of the health and expected life of variouscomponents associated with the drive trains of vehicles, such as cranes,bulldozers, tractors, haulers, backhoes, forklifts, agriculturalequipment, mining equipment, boring and drilling machines, diggingmachines, lifting machines, mixers (e.g., cement mixers), tank trucks,refrigeration trucks, security vehicles (e.g., including safes andsimilar facilities for preserving valuables), underwater vehicles,watercraft, aircraft, automobiles, trucks, trains and the like, as wellas drive trains of moving apparatus, such as assembly lines, lifts,cranes, conveyors, hauling systems, and others. The evaluation of thesensor data with the model of the system geometry and operatingconditions may be useful in identifying unexpected torsion and thetransmission of that torsion from the motor and drive shaft, from thedrive shaft to the universal joint and from the universal joint to oneor more wheel axles.

In an illustrative and non-limiting example, torsional analysis mayfacilitate in the understanding of the health and expected life ofvarious components associated with train/tram wheels and wheel sets. Asdiscussed above, torsional analysis may facilitate in the identificationof stick-slip between the wheels or wheel sets and the rail. Thetorsional analysis in view of the system geometry may facilitate theidentification of torsional vibration due to stick-slip as opposed tothe torsional vibration due to the driving geometry connecting theengine to the drive shaft to the wheel axle.

In embodiments, as illustrated in FIG. 93, the sensors 9406 may be partof the data monitoring device 9400, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 94 and 95, oneor more external sensors 9422, which are not explicitly part of amonitoring device 9416 but rather are new, previously attached to orintegrated into the equipment or component, may be opportunisticallyconnected to or accessed by the monitoring device 9416. The monitoringdevice 9416 may include a controller 9418. The controller 9418 mayinclude a data acquisition circuit 9420, the data storage circuit 9414,the system evaluation circuit 9408 and, optionally, the response circuit9410. The system evaluation circuit 9408 may comprise the torsionalanalysis circuit 9412. The data acquisition circuit 9420 may include oneor more input ports 9424. In embodiments as shown in FIG. 95, the dataacquisition circuit 9420 may further comprise a wireless communicationscircuit 9426. The one or more of the external sensors 9422 may bedirectly connected to the one or more of the input ports 9424 on thedata acquisition circuit 9420 of the controller 9418 or may be accessedby the data acquisition circuit 9420 wirelessly using the wirelesscommunications circuit 9426, such as by a reader, interrogator, or otherwireless connection, such as over a short-distance wireless protocol.The data acquisition circuit 9420 may use the wireless communicationscircuit 9426 to access detection values corresponding to the one or moreof the external sensors 9422 wirelessly or via a separate source or somecombination of these methods.

In embodiments, as illustrated in FIG. 96, a data acquisition circuit9432 may further comprise a multiplexer circuit 9434 as describedelsewhere herein. Outputs from the multiplexer circuit 9434 may beutilized by the system evaluation circuit 9408. The response circuit9410 may have the ability to turn on or off portions of the multiplexorcircuit 9434. The response circuit 9410 may have the ability to controlthe control channels of the multiplexor circuit 9434

The response circuit 9410 may initiate actions based on a componentperformance parameter, a component health value, a component lifeprediction parameter, and the like. The response circuit 9410 mayevaluate the results of the system evaluation circuit 9408 and, based oncertain criteria or the output from various components of the systemevaluation circuit 9408, may initiate an action. The criteria mayinclude identification of torsion on a component by the torsionalanalysis circuit. The criteria may include a sensor's detection valuesat certain frequencies or phases relative to a timer signal where thefrequencies or phases of interest may be based on the equipmentgeometry, equipment control schemes, system input, historical data,current operating conditions, and/or an anticipated response. Thecriteria may include a sensor's detection values at certain frequenciesor phases relative to detection values of a second sensor. The criteriamay include signal strength at certain resonant frequencies/harmonicsrelative to detection values associated with a system tachometer oranticipated based on equipment geometry and operation conditions.Criteria may include a predetermined peak value for a detection valuefrom a specific sensor, a cumulative value of a sensor's correspondingdetection value over time, a change in peak value, a rate of change in apeak value, and/or an accumulated value (e.g., a time spent above/belowa threshold value, a weighted time spent above/below one or morethreshold values, and/or an area of the detected value above/below oneor more threshold values). The criteria may comprise combinations ofdata from different sensors such as relative values, relative changes invalue, relative rates of change in value, relative values over time, andthe like. The relative criteria may change with other data orinformation such as process stage, type of product being processed, typeof equipment, ambient temperature and humidity, external vibrations fromother equipment, and the like. The relative criteria may be reflected inone or more calculated statistics or metrics (including ones generatedby further calculations on multiple criteria or statistics), which inturn may be used for processing (such as on board a data collector or byan external system), such as to be provided as an input to one or moreof the machine learning capabilities described in this disclosure, to acontrol system (which may be on board a data collector or remote, suchas to control selection of data inputs, multiplexing of sensor data,storage, or the like), or as a data element that is an input to anothersystem, such as a data stream or data package that may be available to adata marketplace, a SCADA system, a remote control system, a maintenancesystem, an analytic system, or other system.

Certain embodiments are described herein as detected values exceedingthresholds or predetermined values, but detected values may also fallbelow thresholds or predetermined values—for example where an amount ofchange in the detected value is expected to occur, but detected valuesindicate that the change may not have occurred. Except where the contextclearly indicates otherwise, any description herein describing adetermination of a value above a threshold and/or exceeding apredetermined or expected value is understood to include determinationof a value below a threshold and/or falling below a predetermined orexpected value.

The predetermined acceptable range may be based on anticipated torsionbased on equipment geometry, the geometry of a transfer system, anequipment configuration or control scheme, such as a piston firingsequence, and the like. The predetermined acceptable range may also bebased on historical performance or predicted performance, such as longterm analysis of signals and performance both from the past run and fromthe past several runs. The predetermined acceptable range may also bebased on historical performance or predicted performance, or based onlong term analysis of signals and performance across a plurality ofsimilar equipment and components (both within a specific environment,within an individual company, within multiple companies in the sameindustry and across industries). The predetermined acceptable range mayalso be based on a correlation of sensor data with actual equipment andcomponent performance.

In some embodiments, an alert may be issued based on some of thecriteria discussed above. In embodiments, the relative criteria for analarm may change with other data or information, such as process stage,type of product being processed on equipment, ambient temperature andhumidity, external vibrations from other equipment and the like. In anillustrative and non-limiting example, the response circuit 9410 mayinitiate an alert if a torsion in a component across a plurality ofcomponents exceeds a predetermined maximum value, if there is a changeor rate of change that exceeds a predetermined acceptable range, and/orif an accumulated value based on torsion amplitude and/or frequencyexceeds a threshold.

In embodiments, the response circuit 9410 may cause the data acquisitioncircuit 9432 to enable or disable the processing of detection valuescorresponding to certain sensors based on some of the criteria discussedabove. This may include switching to sensors having different responserates, sensitivity, ranges, and the like; accessing new sensors or typesof sensors, and the like. Switching may be undertaken based on a model,a set of rules, or the like. In embodiments, switching may be undercontrol of a machine learning system, such that switching is controlledbased on one or more metrics of success, combined with input data, overa set of trials, which may occur under supervision of a human supervisoror under control of an automated system. Switching may involve switchingfrom one input port to another (such as to switch from one sensor toanother). Switching may involve altering the multiplexing of data, suchas combining different streams under different circumstances. Switchingmay involve activating a system to obtain additional data, such asmoving a mobile system (such as a robotic or drone system), to alocation where different or additional data is available (such aspositioning an image sensor for a different view or positioning a sonarsensor for a different direction of collection) or to a location wheredifferent sensors can be accessed (such as moving a collector to connectup to a sensor that is disposed at a location in an environment by awired or wireless connection). This switching may be implemented bychanging the control signals for the multiplexor circuit 9434 and/or byturning on or off certain input sections of the multiplexor circuit9434.

The response circuit 9410 may calculate transmission effectiveness basedon differences between a measured and theoretical angular position andvelocity of an output shaft after accounting for the gear ration and anyphase differential between input and output.

The response circuit 9410 may identify equipment or components that aredue for maintenance. The response circuit 9410 may make recommendationsfor the replacement of certain sensors in the future with sensors havingdifferent response rates, sensitivity, ranges, and the like. Theresponse circuit 9410 may recommend design alterations for futureembodiments of the component, the piece of equipment, the operatingconditions, the process, and the like.

In embodiments, the response circuit 9410 may recommend maintenance atan upcoming process stop or initiate a maintenance call. The responsecircuit 9410 may recommend changes in process or operating parameters toremotely balance the piece of equipment. In embodiments, the responsecircuit 9410 may implement or recommend process changes—for example tolower the utilization of a component that is near a maintenanceinterval, operating off-nominally, or failed for purpose but still atleast partially operational, to change the operating speed of acomponent (such as to put it in a lower-demand mode), to initiateamelioration of an issue (such as to signal for additional lubricationof a roller bearing set, or to signal for an alignment process for asystem that is out of balance), and the like.

In embodiments as shown in FIGS. 97, 98, 99, and 100, a data monitoringsystem 9460 may include at least one data monitoring device 9448. Atleast one of the data monitoring device 9448 may include the sensors9406 and a controller 9438 comprising the data acquisition circuit 9404,the system evaluation circuit 9408, the data storage circuit 9414, and acommunications circuit 9442. The system evaluation circuit 9408 mayinclude the torsional analysis circuit 9412. There may also be anoptional response circuit as described above and elsewhere herein. Thesystem evaluation circuit 9408 may periodically share data with thecommunication circuit 9442 for transmittal to a remote server 9440 toenable the tracking of component and equipment performance over time andunder varying conditions by a monitoring application 9446. Becauserelevant operating conditions and/or failure modes may occur as sensorvalues approach one or more criteria, the system evaluation circuit 9408may share data with a communication circuit 9462 for transmittal to theremote server 9440 based on the fit of data relative to one or morecriteria. Based on one sensor input meeting or approaching specifiedcriteria or range, the system evaluation circuit 9408 may shareadditional data such as RPMs, component loads, temperatures, pressures,vibrations, and the like for transmittal. The system evaluation circuit9408 may share data at a higher data rate for transmittal to enablegreater granularity in processing on the remote server. In embodiments,as shown in FIG. 97, the communications circuit 9442 may communicatedata directly to the remote server 9440. In embodiments, as shown inFIG. 98, the communications circuit 9442 may communicate data to anintermediate computer 9450 which may include a processor 9452 running anoperating system 9454 and a data storage circuit 9456.

In embodiments, as illustrated in FIGS. 99 and 100, a data collectionsystem 9458 may have a plurality of the monitoring devices 9448collecting data on multiple components in a single piece of equipment,collecting data on the same component across a plurality of pieces ofequipment (both the same and different types of equipment) in the samefacility as well as collecting data from monitoring devices in multiplefacilities. The monitoring application 9446 on the remote server 9440may receive and store one or more of detection values, timing signalsand data coming from a plurality of the various monitoring devices 9448.In embodiments, as shown in FIG. 99, the communications circuit 9442 maycommunicate data directly to the remote server 9440. In embodiments, asshown in FIG. 100, the communications circuit 9442 may communicate datato an intermediate computer 9450, which may include the processor 9452running the operating system 9454 and the data storage circuit 9456.There may be an individual intermediate computer 9450 associated witheach of the monitoring device 9264 or an individual intermediatecomputer 9450 may be associated with a plurality of the monitoringdevices 9448 where the intermediate computer 9450 may collect data froma plurality of data monitoring devices and send the cumulative data tothe remote server 9440.

The monitoring application 9446 may select subsets of detection values,timing signals, data, product performance and the like to be jointlyanalyzed. Subsets for analysis may be selected based on component type,component materials, or a single type of equipment in which a componentis operating. Subsets for analysis may be selected or grouped based oncommon operating conditions or operational history such as size of load,operational condition (e.g., intermittent, continuous), operating speedor tachometer, common ambient environmental conditions such as humidity,temperature, air or fluid particulate, and the like. Subsets foranalysis may be selected based on common anticipated state information.Subsets for analysis may be selected based on the effects of othernearby equipment such as nearby machines rotating at similarfrequencies, nearby equipment producing electromagnetic fields, nearbyequipment producing heat, nearby equipment inducing movement orvibration, nearby equipment emitting vapors, chemicals or particulates,or other potentially interfering or intervening effects.

The monitoring application 9446 may analyze a selected subset. In anillustrative example, data from a single component may be analyzed overdifferent time periods such as one operating cycle, cycle to cyclecomparisons, trends over several operating cycles/times such as a month,a year, the life of the component or the like. Data from multiplecomponents of the same type may also be analyzed over different timeperiods. Trends in the data such as changes in frequency or amplitudemay be correlated with failure and maintenance records associated withthe same component or piece of equipment. Trends in the data such aschanging rates of change associated with start-up or different points inthe process may be identified. Additional data may be introduced intothe analysis such as output product quality, output quantity (such asper unit of time), indicated success or failure of a process, and thelike. Correlation of trends and values for different types of data maybe analyzed to identify those parameters whose short-term analysis mightprovide the best prediction regarding expected performance. The analysismay identify model improvements to the model for anticipated stateinformation, recommendations around sensors to be used, positioning ofsensors and the like. The analysis may identify additional data tocollect and store. The analysis may identify recommendations regardingneeded maintenance and repair and/or the scheduling of preventativemaintenance. The analysis may identify recommendations around purchasingreplacement components and the timing of the replacement of thecomponents. The analysis may identify recommendations regarding futuregeometry changes to reduce torsion on components. The analysis mayresult in warning regarding dangers of catastrophic failure conditions.This information may be transmitted back to the monitoring device toupdate types of data collected and analyzed locally or to influence thedesign of future monitoring devices.

In embodiments, the monitoring application 9446 may have access toequipment specifications, equipment geometry, component specifications,component materials, anticipated state information for a plurality ofcomponent types, operational history, historical detection values,component life models and the like for use analyzing the selected subsetusing rule-based or model-based analysis. In embodiments, the monitoringapplication 9446 may feed a neural net with the selected subset to learnto recognize various operating states, health states (e.g., lifetimepredictions) and fault states utilizing deep learning techniques. Inembodiments, a hybrid of the two techniques (model-based learning anddeep learning) may be used.

In an illustrative and non-limiting example, the health of the rotatingcomponents on conveyors and lifters in an assembly line may be monitoredusing the torsional analysis techniques, data monitoring devices anddata collection systems described herein.

In an illustrative and non-limiting example, the health the rotatingcomponents in water pumps on industrial vehicles may be monitored usingthe torsional analysis techniques, data monitoring devices and datacollection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents in compressors in gas handling systems may be monitored usingthe data monitoring devices and data collection systems describedherein.

In an illustrative and non-limiting example, the health of the rotatingcomponents in compressors situated in the gas and oil fields may bemonitored using the data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of the rotatingcomponents in factory air conditioning units may be evaluated using thetechniques, data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of the rotatingcomponents in factory mineral pumps may be evaluated using thetechniques, data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of the rotatingcomponents such as shafts, bearings, and gears in drilling machines andscrew drivers situated in the oil and gas fields may be evaluated usingthe torsional analysis techniques, data monitoring devices and datacollection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as shafts, bearings, gears, and rotors of motorssituated in the oil and gas fields may be evaluated using the torsionalanalysis techniques, data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as blades, screws and other components of pumps situatedin the oil and gas fields may be evaluated using the torsional analysistechniques, data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as shafts, bearings, motors, rotors, stators, gears, andother components of vibrating conveyors situated in the oil and gasfields may be evaluated using the torsional analysis techniques, datamonitoring devices and data collection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as bearings, shafts, motors, rotors, stators, gears, andother components of mixers situated in the oil and gas fields may beevaluated using the torsional analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as bearings, shafts, motors, rotors, stators, gears, andother components of centrifuges situated in oil and gas refineries maybe evaluated using the torsional analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as bearings, shafts, motors, rotors, stators, gears, andother components of refining tanks situated in oil and gas refineriesmay be evaluated using the torsional analysis techniques, datamonitoring devices and data collection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as bearings, shafts, motors, rotors, stators, gears, andother components of rotating tank/mixer agitators to promote chemicalreactions deployed in chemical and pharmaceutical production lines maybe evaluated using the torsional analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as bearings, shafts, motors, rotors, stators, gears, andother components of mechanical/rotating agitators to promote chemicalreactions deployed in chemical and pharmaceutical production lines maybe evaluated using the torsional analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as bearings, shafts, motors, rotors, stators, gears, andother components of propeller agitators to promote chemical reactionsdeployed in chemical and pharmaceutical production lines may beevaluated using the torsional analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of bearings andassociated shafts, motors, rotors, stators, gears, and other componentsof vehicle steering mechanisms may be evaluated using the torsionalanalysis techniques, data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of bearings andassociated shafts, motors, rotors, stators, gears, and other componentsof vehicle engines may be evaluated using the torsional analysistechniques, data monitoring devices and data collection systemsdescribed herein.

In embodiments, a monitoring device for estimating an anticipatedlifetime of a rotating component in an industrial machine may comprise adata acquisition circuit structured to interpret a plurality ofdetection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors. Inembodiments, the plurality of input sensors comprises at least one of anangular position sensor, an angular velocity sensor and an angularacceleration sensor positioned to measure the rotating component; a datastorage circuit structured to store specifications, system geometry, andanticipated state information for a plurality of rotating components,store historical component performance and buffer the plurality ofdetection values for a predetermined length of time; and a torsionalanalysis circuit structured to utilize transitory signal analysis toanalyze the buffered detection values relative to the rotating componentspecifications and anticipated state information resulting in theidentification of torsional vibration; and a system analysis circuitstructured to utilize the identified torsional vibration and at leastone of an anticipated state, historical data and a system geometry toidentify an anticipated lifetime of the rotating component. Inembodiments, the monitoring device may further comprise a responsecircuit to perform at least one operation in response to the anticipatedlifetime of the rotating component. In embodiments, the plurality ofinput sensors includes at least two sensors selected from the groupconsisting of a temperature sensor, a load sensor, an optical vibrationsensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor,an accelerometer, a tri-axial vibration sensor, a tachometer, and thelike. At least one operation may comprise issuing at least one of analert and a warning, storing additional data in the data storagecircuit, ordering a replacement of the rotating component, schedulingreplacement of the rotating component, recommending alternatives to therotating component, and the like.

In embodiments, a monitoring device for evaluating the health of arotating component in an industrial machine may comprise a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors. In embodiments, the pluralityof input sensors comprises at least one of an angular position sensor,an angular velocity sensor and an angular acceleration sensor positionedto measure the rotating component; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor a plurality of rotating components, store historical componentperformance and buffer the plurality of detection values for apredetermined length of time; and a torsional analysis circuitstructured to utilize transitory signal analysis to analyze the buffereddetection values relative to the rotating component specifications andanticipated state information resulting in the identification oftorsional vibration; and a system analysis circuit structured to utilizethe identified torsional vibration and at least one of an anticipatedstate, historical data and a system geometry to identify the health ofthe rotating component. In embodiments, the monitoring device mayfurther comprise a response circuit to perform at least one operation inresponse to the health of the rotating component. The plurality of inputsensors may include at least two sensors selected from the groupconsisting of a temperature sensor, a load sensor, an optical vibrationsensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor,an accelerometer, a tri-axial vibration sensor a tachometer, and thelike. The monitoring device may issue an alert and an alarm, such as theat least one operation storing additional data in the data storagecircuit, ordering a replacement of the rotating component, schedulingreplacement of the rotating component, recommending alternatives to therotating component, and the like.

In embodiments, a monitoring device for evaluating the operational stateof a rotating component in an industrial machine may comprise a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors. In embodiments, the pluralityof input sensors comprises at least one of an angular position sensor,an angular velocity sensor and an angular acceleration sensor positionedto measure the rotating component; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor a plurality of rotating components, store historical componentperformance and buffer the plurality of detection values for apredetermined length of time; and a torsional analysis circuitstructured to utilize transitory signal analysis to analyze the buffereddetection values relative to the rotating component specifications andanticipated state information resulting in the identification oftorsional vibration; and a system analysis circuit structured to utilizethe identified torsional vibration and at least one of an anticipatedstate, historical data and a system geometry to identify the operationalstate of the rotating component. In embodiments, the operational statemay be a current or future operational state. A response circuit mayperform at least one operation in response to the operational state ofthe rotating component. The at least one operation may store additionaldata in the data storage circuit, order a replacement of the rotatingcomponent, schedule a replacement of the rotating component,recommending alternatives to the rotating component, and the like.

In embodiments, s monitoring device for evaluating the operational stateof a rotating component in an industrial machine may include a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors. In embodiments, the pluralityof input sensors comprises at least one of an angular position sensor,an angular velocity sensor and an angular acceleration sensor positionedto measure the rotating component; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor a plurality of rotating components, store historical componentperformance and buffer the plurality of detection values for apredetermined length of time; and a torsional analysis circuitstructured to utilize transitory signal analysis to analyze the buffereddetection values relative to the rotating component specifications andanticipated state information resulting in the identification oftorsional vibration; and a system analysis circuit structured to utilizethe identified torsional vibration and at least one of an anticipatedstate, historical data and a system geometry to identify the operationalstate of the rotating component. In embodiments, the data acquisitioncircuit comprises a multiplexer circuit whereby alternative combinationsof the detection values may be selected based on at least one of userinput, a detected state and a selected operating parameter for amachine. The operational state may be a current or future operationalstate. The at least one operation may enable or disable one or moreportions of the multiplexer circuit, or altering the multiplexer controllines. The data acquisition circuit may include at least two multiplexercircuits and the at least one operation comprises changing connectionsbetween the at least two multiplexer circuits.

In embodiments, a system for evaluating an operational state a rotatingcomponent in a piece of equipment may comprise a data acquisitioncircuit structured to interpret a plurality of detection values, each ofthe plurality of detection values corresponding to at least one of aplurality of input sensors. In embodiments, the plurality of inputsensors comprises at least one of an angular position sensor, an angularvelocity sensor and an angular acceleration sensor positioned to measurethe rotating component; a data storage circuit structured to storespecifications, system geometry, and anticipated state information for aplurality of rotating components, store historical component performanceand buffer the plurality of detection values for a predetermined lengthof time; and a torsional analysis circuit structured to utilizetransitory signal analysis to analyze the buffered detection valuesrelative to the rotating component specifications and anticipated stateinformation resulting in identification of any torsional vibration; asystem analysis circuit structured to utilize the torsional vibrationand at least one of an anticipated state, historical data and a systemgeometry to identify the operational state of the rotating component;and a communication module enabled to communicate the operational stateof the rotating component, the torsional vibration and detection valuesto a remote server. In embodiments, the detection values communicatedare based partly on the operational state of the rotating component andthe torsional vibration; and a monitoring application on the remoteserver structured to receive, store and jointly analyze a subset of thedetection values from the monitoring devices. The analysis of the subsetof detection values may include transitory signal analysis to identifythe presence of high frequency torsional vibration. The monitoringapplication may be structured to subset detection values based on oneof: operational state, torsional vibration, type of the rotatingcomponent, operational conditions under which detection values weremeasured, and type or equipment. The analysis of the subset of detectionvalues may include feeding a neural net with the subset of detectionvalues and supplemental information to learn to recognize variousoperating states, health states and fault states utilizing deep learningtechniques. The supplemental information may include one of componentspecification, component performance, equipment specification, equipmentperformance, maintenance records, repair records an anticipated statemodel, and the like. The operational state may include a current orfuture operational state. The monitoring device may include a responsecircuit to perform at least one operation in response to the operationalstate of the rotating component. The at least one operation may includestoring additional data in the data storage circuit.

In embodiments, a system for evaluating the health of a rotatingcomponent in a piece of equipment may comprise a data acquisitioncircuit structured to interpret a plurality of detection values, each ofthe plurality of detection values corresponding to at least one of aplurality of input sensors. In embodiments, the plurality of inputsensors comprises at least one of: an angular position sensor, anangular velocity sensor and an angular acceleration sensor positioned tomeasure the rotating component; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor a plurality of rotating components, store historical componentperformance and buffer the plurality of detection values for apredetermined length of time; and a torsional analysis circuitstructured to utilize transitory signal analysis to analyze the buffereddetection values relative to the rotating component specifications andanticipated state information resulting in identification of torsionalvibration; a system analysis circuit structured to utilize the torsionalvibration and at least one of an anticipated state, historical data anda system geometry to identify the health of the rotating component; anda communication module enabled to communicate the health of the rotatingcomponent, the torsional vibrations and detection values to a remoteserver. In embodiments, the detection values communicated are basedpartly on the health of the rotating component and the torsionalvibration; and a monitoring application on the remote server structuredto receive, store and jointly analyze a subset of the detection valuesfrom the monitoring devices. In embodiments, the analysis of the subsetof detection values may include transitory signal analysis to identifythe presence of high frequency torsional vibration. The monitoringapplication may be structured to subset detection values. The analysisof the subset of detection values may include feeding a neural net withthe subset of detection values and supplemental information to learn torecognize various operating states, health states and fault statesutilizing deep learning techniques. The supplemental information mayinclude one of component specification, component performance, equipmentspecification, equipment performance, maintenance records, repairrecords and an anticipated state model. The operational state may be acurrent or future operational state. A response circuit may perform atleast one operation in response to the health of the rotating component.

In embodiments, a system for estimating an anticipated lifetime of arotating component in a piece of equipment may comprise a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors. In embodiments, the pluralityof input sensors comprises at least one of an angular position sensor,an angular velocity sensor and an angular acceleration sensor positionedto measure the rotating component; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor a plurality of rotating components, store historical componentperformance and buffer the plurality of detection values for apredetermined length of time; and a torsional analysis circuitstructured to utilize transitory signal analysis to analyze the buffereddetection values relative to the rotating component specifications andanticipated state information resulting in identification of torsionalvibration; a system analysis circuit structured to utilize the torsionalvibration and at least one of an anticipated state, historical data anda system geometry to identify an anticipated life the rotatingcomponent; and a communication module enabled to communicate theanticipated life of the rotating component, the torsional vibrations anddetection values to a remote server. In embodiments, the detectionvalues communicated are based partly on the anticipated life of therotating component and the torsional vibration; and a monitoringapplication on the remote server structured to receive, store andjointly analyze a subset of the detection values from the monitoringdevices. In embodiments, the analysis of the subset of detection valuesmay include transitory signal analysis to identify the presence of highfrequency torsional vibration. The monitoring application may bestructured to subset detection values based on one of anticipated lifeof the rotating component, torsional vibration, type of the rotatingcomponent, operational conditions under which detection values weremeasured, and type of equipment. The analysis of the subset of detectionvalues may include feeding a neural net with the subset of detectionvalues and supplemental information to learn to recognize variousoperating states, health states, life expectancies and fault statesutilizing deep learning techniques. The supplemental information mayinclude one of component specification, component performance, equipmentspecification, equipment performance, maintenance records, repairrecords and an anticipated state model. The monitoring device mayinclude a response circuit to perform at least one operation in responseto the anticipated life of the rotating component. The at least oneoperation may include one of ordering a replacement of the rotatingcomponent, scheduling replacement of the rotating component, andrecommending alternatives to the rotating component.

In embodiments, a system for evaluating the health of a variablefrequency motor in an industrial environment may comprise a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors. In embodiments, the pluralityof input sensors comprises at least one of an angular position sensor,an angular velocity sensor and an angular acceleration sensor positionedto measure the rotating component; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor a plurality of rotating components, store historical componentperformance and buffer the plurality of detection values for apredetermined length of time; and a torsional analysis circuitstructured to utilize transitory signal analysis to analyze the buffereddetection values relative to the rotating component specifications andanticipated state information resulting in identification of torsionalvibration; a system analysis circuit structured to utilize the torsionalvibration and at least one of an anticipated state, historical data anda system geometry to identify a motor health parameter; and acommunication module enabled to communicate the motor health parameter,the torsional vibrations and detection values to a remote server. Inembodiments, the detection values communicated are based partly on themotor health parameter and the torsional vibration; and a monitoringapplication on the remote server structured to receive, store andjointly analyze a subset of the detection values from the monitoringdevices.

In embodiments, a system for data collection, processing, and torsionalanalysis of a rotating component in an industrial environment maycomprise a data acquisition circuit structured to interpret a pluralityof detection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors. Inembodiments, the plurality of input sensors comprises at least one of anangular position sensor, an angular velocity sensor and an angularacceleration sensor positioned to measure the rotating component; astreaming circuit for streaming at least a subset of the acquireddetection values to a remote learning system; and a remote learningsystem including a torsional analysis circuit structured to analyze thedetection values relative to a machine-based understanding of the stateof the at least one rotating component. The machine-based understandingmay be developed based on a model of the rotating component thatdetermines a state of the at least one rotating component based at leastin part on the relationship of the behavior of the rotating component toan operating frequency of a component of the industrial machine. Thestate of the at least one rotating component may be at least one of anoperating state, a health state, a predicted lifetime state and a faultstate. The machine-based understanding may be developed based byproviding inputs to a deep learning machine. In embodiments, the inputscomprise a plurality of streams of detection values for a plurality ofrotating components and a plurality of measured state values for theplurality of rotating components. The state of the at least one rotatingcomponent may be at least one of an operating state, a health state, apredicted lifetime state and a fault state.

In embodiments, information about the health or other status or stateinformation of or regarding a component or piece of industrial equipmentmay be obtained by monitoring the condition of various componentsthroughout a process. Monitoring may include monitoring the amplitude ofa sensor signal measuring attributes such as temperature, humidity,acceleration, displacement and the like. An embodiment of a datamonitoring device 9700 is shown in FIG. 101 and may include a pluralityof sensors 9706 communicatively coupled to a controller 9702. Thecontroller 9702 may include a data acquisition circuit 9704, a signalevaluation circuit 9708, a data storage circuit 9716 and a responsecircuit 9710. The signal evaluation circuit 9708 may comprise a circuitfor detecting a fault in one or more sensors, or a set of sensors, suchas an overload detection circuit 9712, a sensor fault detection circuit9714, or both. Additionally, the signal evaluation circuit 9708 mayoptionally comprise one or more of a peak detection circuit, a phasedetection circuit, a bandpass filter circuit, a frequency transformationcircuit, a frequency analysis circuit, a phase lock loop circuit, atorsional analysis circuit, a bearing analysis circuit, and the like.

The plurality of the sensors 9706 may be wired to ports on the dataacquisition circuit 9704. The plurality of the sensors 9706 may bewirelessly connected to the data acquisition circuit 9704. The dataacquisition circuit 9704 may be able to access detection valuescorresponding to the output of at least one of the plurality of thesensors 9706 where the sensors 9706 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.

The selection of the plurality of the sensors 9706 for the datamonitoring device 9700 designed for a specific component or piece ofequipment may depend on a variety of considerations such asaccessibility for installing new sensors, incorporation of sensors inthe initial design, anticipated operational and failure conditions,resolution desired at various positions in a process or plant,reliability of the sensors, and the like. The impact of a failure, timeresponse of a failure (e.g., warning time and/or off-nominal modesoccurring before failure), likelihood of failure, and/or sensitivityrequired and/or difficulty to detection failure conditions may drive theextent to which a component or piece of equipment is monitored with moresensors and/or higher capability sensors being dedicated to systemswhere unexpected or undetected failure would be costly or have severeconsequences.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, thesensors 9706 may comprise, without limitation, one or more of thefollowing: a vibration sensor, a thermometer, a hygrometer, a voltagesensor and/or a current sensor (for the component and/or other sensorsmeasuring the component), an accelerometer, a velocity detector, a lightor electromagnetic sensor (e.g., determining temperature, compositionand/or spectral analysis, and/or object position or movement), an imagesensor, a structured light sensor, a laser-based image sensor, a thermalimager, an acoustic wave sensor, a displacement sensor, a turbiditymeter, a viscosity meter, a axial load sensor, a radial load sensor, atri-axial sensor, an accelerometer, a speedometer, a tachometer, a fluidpressure meter, an air flow meter, a horsepower meter, a flow ratemeter, a fluid particle detector, an optical (laser) particle counter,an ultrasonic sensor, an acoustical sensor, a heat flux sensor, agalvanic sensor, a magnetometer, a pH sensor, and the like, including,without limitation, any of the sensors described throughout thisdisclosure and the documents incorporated by reference.

The sensors 9706 may provide a stream of data over time that has a phasecomponent, such as relating to acceleration or vibration, allowing forthe evaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors9706 may provide a stream of data that is not conventionallyphase-based, such as temperature, humidity, load, and the like. Thesensors 9706 may provide a continuous or near continuous stream of dataover time, periodic readings, event-driven readings, and/or readingsaccording to a selected interval or schedule.

In embodiments, as illustrated in FIG. 101, the sensors 9706 may be partof the data monitoring device 9700, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 102 and 103, oneor more external sensors 9724, which are not explicitly part of amonitoring device 9718 but rather are new, previously attached to orintegrated into the equipment or component, may be opportunisticallyconnected to or accessed by the monitoring device 9718. The monitoringdevice may include a data acquisition circuit 9722, the signalevaluation circuit 9708, the data storage circuit 9716 and the responsecircuit 9710. The signal evaluation circuit 9708 may comprise theoverload detection circuit 9712, the sensor fault detection circuit9714, or both.

Additionally, the signal evaluation circuit 9708 may optionally compriseone or more of a peak detection circuit, a phase detection circuit, abandpass filter circuit, a frequency transformation circuit, a frequencyanalysis circuit, a phase lock loop circuit, a torsional analysiscircuit, a bearing analysis circuit, and the like. The data acquisitioncircuit 9722 may include one or more input ports 9726.

The one or more of the external sensors 9724 may be directly connectedto the one or more of the input ports 9726 on the data acquisitioncircuit 9722 of a controller 9720 or may be accessed by the dataacquisition circuit 9722 wirelessly, such as by a reader, interrogator,or other wireless connection, such as over a short-distance wirelessprotocol. In embodiments, as shown in FIG. 103, the data acquisitioncircuit 9722 may further comprise a wireless communication circuit 9730.The data acquisition circuit 9722 may use the wireless communicationcircuit 9730 to access detection values corresponding to the one or moreof the external sensors 9724 wirelessly or via a separate source or somecombination of these methods.

In embodiments, the data storage circuit 9716 may be structured to storesensor specifications, anticipated state information and detectedvalues. The data storage circuit 9716 may provide specifications andanticipated state information to the signal evaluation circuit 9708.

In embodiments, the overload detection circuit 9712 may detect sensoroverload by comparing the detected value associated with the sensor witha detected value associated with a sensor having a greater range/lowerresolution monitoring the same component/attribute. Inconsistencies inmeasured value may indicate that the higher resolution sensor may beoverloaded. In embodiments, the overload detection circuit 9712 maydetect sensor overload by evaluating consistency of sensor reading withreadings from other sensor data (monitoring the same or differentaspects of the component/piece of equipment. In embodiments, theoverload detection circuit 9712 may detect sensor overload by evaluatingdata collected by other sensors to identify conditions likely to resultin sensor overload (e.g., heat flux sensor data indicative of thelikelihood of overloading a sensor in a given location, accelerometerdata indicating a likelihood of overloading a velocity sensor, and thelike). In embodiments, the overload detection circuit 9712 may detectsensor overload by identifying flat line output following a risingtrend. In embodiments, the overload detection circuit 9712 may detectsensor overload by transforming the sensor data to frequency data, usingfor example a Fast Fourier Transform (FFT), and then looking for a“ski-jump” in the frequency data which may result from the data beingclipped due to an overloaded sensor. The sensor fault detection circuit9714 may identify failure of the sensor itself, sensor health, orpotential concerns regarding validity of sensor data. Rate of valuechange may be used to identify failure of the sensor itself. Forexample, a sudden jump to a maximum output may indicate a failure in thesensor rather than an overload of the sensor. In embodiments, theoverload detection circuit 9712 and/or the sensor fault detectioncircuit 9714 may utilize sensor specifications, anticipated stateinformation, sensor models and the like in the identification of sensoroverload, failure, error, invalid data, and the like. In embodiments,the overload detection circuit 9712 or the sensor fault detectioncircuit 9714 may use detection values from other sensors and output fromadditional components such as a peak detection circuit and/or a phasedetection circuit and/or a bandpass filter circuit and/or a frequencytransformation circuit and/or a frequency analysis circuit and/or aphase lock loop circuit and the like to identify potential sources forthe identified sensor overload, sensor faults, sensor failure, or thelike. Sources or factors involved in sensor overload may includelimitations on sensor range, sensor resolution, and sensor samplingfrequency. Sources of apparent sensor overload may be due to a range,resolution or sampling frequency of a multiplexor supplying detectionvalues associated with the sensor. Sources of factors involved inapparent sensor faults or failures may include environmental conditions;for example, excessive heat or cold may be associated with damage tosemiconductor-based sensors, which may result in erratic sensor data,failure of a sensor to produce data, data that appears out of the rangeof normal behavior (e.g., large, discrete jumps in temperature for asystem that does not normally experience such changes). Surges incurrent and/or voltage may be associated with damage to electricallyconnected sensors with sensitive components. Excessive vibration mayresult in physical damage to sensitive components of a sensor such aswires and/or connectors. An impact, which may be indicated by suddenacceleration or acoustical data may result in physical damage to asensor with sensitive components such as wires and/or connectors. Arapid increase in humidity in the environment surrounding a sensor or anabsence of oxygen may indicate water damage to a sensor. A suddenabsence of signal from a sensor may be indicative of sensordisconnection which may due to vibration, impact and the like. A sensorthat requires power may run out of battery power or be disconnected froma power source. In embodiments, the overload detection circuit 9712 orthe sensor fault detection circuit 9714 may output a sensor status wherethe sensor status may be one of sensor overload, sensor failure, sensorfault, sensor healthy, and the like. The sensor fault detection circuit9714 may determine one of a sensor fault status and a sensor validitystatus.

In embodiments, as illustrated in FIG. 104, the data acquisition circuit9722 may further comprise a multiplexer circuit 9731 as describedelsewhere herein. Outputs from the multiplexer circuit 9731 may beutilized by the signal evaluation circuit 9708. The response circuit9710 may have the ability to turn on or off portions of the multiplexorcircuit 9731. The response circuit 9710 may have the ability to controlthe control channels of the multiplexor circuit 9731.

In embodiments, the response circuit 9710 may initiate a variety ofactions based on the sensor status provided by the overload detectioncircuit 9712. The response circuit 9710 may continue using the sensor ifthe sensor status is “sensor healthy.” The response circuit 9710 mayadjust a sensor scaling value (e.g., from 100 mV/gram to 10 mV/gram).The response circuit 9710 may increase an acquisition range for analternate sensor. The response circuit 9710 may back sensor data out ofprevious calculations and evaluations such as bearing analysis,torsional analysis and the like. The response circuit 9710 may useprojected or anticipated data (based on data acquired prior tooverload/failure) in place of the actual sensor data for calculationsand evaluations such as bearing analysis, torsional analysis and thelike. The response circuit 9710 may issue an alarm. The response circuit9710 may issue an alert that may comprise notification that the sensoris out of range together with information regarding the extent of theoverload such as “overload range-data response may not be reliableand/or linear”, “destructive range-sensor may be damaged,” and the like.The response circuit 9710 may issue an alert where the alert maycomprise information regarding the effect of sensor load such as “unableto monitor machine health” due to sensor overload/failure,” and thelike.

In embodiments, the response circuit 9710 may cause the data acquisitioncircuit 9704 to enable or disable the processing of detection valuescorresponding to certain sensors based on the sensor statues describedabove. This may include switching to sensors having different responserates, sensitivity, ranges, and the like; accessing new sensors or typesof sensors, accessing data from multiple sensors, recruiting additionaldata collectors (such as routing the collectors to a point of work,using routing methods and systems disclosed throughout this disclosureand the documents incorporated by reference) and the like. Switching maybe undertaken based on a model, a set of rules, or the like. Inembodiments, switching may be under control of a machine learningsystem, such that switching is controlled based on one or more metricsof success, combined with input data, over a set of trials, which mayoccur under supervision of a human supervisor or under control of anautomated system. Switching may involve switching from one input port toanother (such as to switch from one sensor to another). Switching mayinvolve altering the multiplexing of data, such as combining differentstreams under different circumstances. Switching may involve activatinga system to obtain additional data, such as moving a mobile system (suchas a robotic or drone system), to a location where different oradditional data is available (such as positioning an image sensor for adifferent view or positioning a sonar sensor for a different directionof collection) or to a location where different sensors can be accessed(such as moving a collector to connect up to a sensor that is disposedat a location in an environment by a wired or wireless connection). Thisswitching may be implemented by changing the control signals for themultiplexor circuit 9731 and/or by turning on or off certain inputsections of the multiplexor circuit 9731.

In embodiments, the response circuit 9710 may make recommendations forthe replacement of certain sensors in the future with sensors havingdifferent response rates, sensitivity, ranges, and the like. Theresponse circuit 9710 may recommend design alterations for futureembodiments of the component, the piece of equipment, the operatingconditions, the process, and the like.

In embodiments, the response circuit 9710 may recommend maintenance atan upcoming process stop or initiate a maintenance call where themaintenance may include the replacement of the sensor with the same oran alternate type of sensor having a different response rate,sensitivity, range and the like. In embodiments, the response circuit9710 may implement or recommend process changes—for example to lower theutilization of a component that is near a maintenance interval,operating off-nominally, or failed for purpose but still at leastpartially operational, to change the operating speed of a component(such as to put it in a lower-demand mode), to initiate amelioration ofan issue (such as to signal for additional lubrication of a rollerbearing set, or to signal for an alignment process for a system that isout of balance), and the like.

In embodiments, the signal evaluation circuit 9708 and/or the responsecircuit 9710 may periodically store certain detection values in the datastorage circuit 9716 to enable the tracking of component performanceover time. In embodiments, based on sensor status, as describedelsewhere herein recently measured sensor data and related operatingconditions such as RPMs, component loads, temperatures, pressures,vibrations or other sensor data of the types described throughout thisdisclosure in the data storage circuit 9716 to enable the backing out ofoverloaded/failed sensor data. The signal evaluation circuit 9708 maystore data at a higher data rate for greater granularity in futureprocessing, the ability to reprocess at different sampling rates, and/orto enable diagnosing or post-processing of system information whereoperational data of interest is flagged, and the like.

In embodiments as shown in FIGS. 105, 106, 107, and 108, the datamonitoring system 9726 may include at least one of a data monitoringdevice 9728. At least one of the data monitoring device 9728 may includethe sensors 9706 and the controller 9730 comprising the data acquisitioncircuit 9704, the signal evaluation circuit 9708, the data storagecircuit 9716, and a communication circuit 9754 to allow data andanalysis to be transmitted to a monitoring application 9736 on a remoteserver 9734. The signal evaluation circuit 9708 may include at least theoverload detection circuit 9712. The signal evaluation circuit 9708 mayperiodically share data with a communication circuit 9732 fortransmittal to the remote server 9734 to enable the tracking ofcomponent and equipment performance over time and under varyingconditions by the monitoring application 9736. Based on the sensorstatus, the signal evaluation circuit 9708 and/or the response circuit9710 may share data with the communication circuit 9732 for transmittalto the remote server 9734 based on the fit of data relative to one ormore criteria. Data may include recent sensor data and additional datasuch as RPMs, component loads, temperatures, pressures, vibrations, andthe like for transmittal. The signal evaluation circuit 9708 may sharedata at a higher data rate for transmittal to enable greater granularityin processing on the remote server.

In embodiments, as shown in FIG. 105, the communication circuit 9732 maycommunicate data directly to the remote server 9734. In embodiments asshown in FIG. 106, the communication circuit 9732 may communicate datato an intermediate computer 9738 which may include a processor 9740running an operating system 9742 and a data storage circuit 9744.

In embodiments, as illustrated in FIGS. 107 and 108, a data collectionsystem 9746 may have a plurality of the monitoring devices 9728collecting data on multiple components in a single piece of equipment,collecting data on the same component across a plurality of pieces ofequipment, (both the same and different types of equipment) in the samefacility as well as collecting data from monitoring devices in multiplefacilities. The monitoring application 9736 on the remote server 9734may receive and store one or more of detection values, timing signalsand data coming from a plurality of the various monitoring devices 9728.

In embodiments, as shown in FIG. 107, the communication circuit 9732 maycommunicated data directly to the remote server 9734. In embodiments, asshown in FIG. 108, the communication circuit 9732 may communicate datato the intermediate computer 9738 which may include the processor 9740running the operating system 9742 and the data storage circuit 9744.There may be the individual intermediate computer 9738 associated witheach monitoring device 9728 or the individual intermediate computer 9738may be associated with a plurality of the monitoring devices 9728 wherethe intermediate computer 9738 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server9734. Communication to the remote server 9734 may be streaming, batch(e.g., when a connection is available) or opportunistic.

The monitoring application 9736 may select subsets of the detectionvalues to be jointly analyzed. Subsets for analysis may be selectedbased on a single type of sensor, component or a single type ofequipment in which a component is operating. Subsets for analysis may beselected or grouped based on common operating conditions such as size ofload, operational condition (e.g., intermittent, continuous), operatingspeed or tachometer, common ambient environmental conditions such ashumidity, temperature, air or fluid particulate, and the like. Subsetsfor analysis may be selected based on the effects of other nearbyequipment such as nearby machines rotating at similar frequencies,nearby equipment producing electromagnetic fields, nearby equipmentproducing heat, nearby equipment inducing movement or vibration, nearbyequipment emitting vapors, chemicals or particulates, or otherpotentially interfering or intervening effects.

In embodiments, the monitoring application 9736 may analyze the selectedsubset. In an illustrative example, data from a single sensor may beanalyzed over different time periods such as one operating cycle,several operating cycles, a month, a year, the life of the component orthe like. Data from multiple sensors of a common type measuring a commoncomponent type may also be analyzed over different time periods. Trendsin the data such as changing rates of change associated with start-up ordifferent points in the process may be identified. Correlation of trendsand values for different sensors may be analyzed to identify thoseparameters whose short-term analysis might provide the best predictionregarding expected sensor performance. This information may betransmitted back to the monitoring device to update sensor models,sensor selection, sensor range, sensor scaling, sensor samplingfrequency, types of data collected and analyzed locally or to influencethe design of future monitoring devices.

In embodiments, the monitoring application 9736 may have access toequipment specifications, equipment geometry, component specifications,component materials, anticipated state information for a plurality ofsensors, operational history, historical detection values, sensor lifemodels and the like for use analyzing the selected subset usingrule-based or model-based analysis. The monitoring application 9736 mayprovide recommendations regarding sensor selection, additional data tocollect, or data to store with sensor data. The monitoring application9736 may provide recommendations regarding scheduling repairs and/ormaintenance. The monitoring application 9736 may provide recommendationsregarding replacing a sensor. The replacement sensor may match thesensor being replaced or the replacement sensor may have a differentrange, sensitivity, sampling frequency and the like.

In embodiments, the monitoring application 9736 may include a remotelearning circuit structured to analyze sensor status data (e.g., sensoroverload, sensor faults, sensor failure) together with data from othersensors, failure data on components being monitored, equipment beingmonitored, product being produced, and the like. The remote learningsystem may identify correlations between sensor overload and data fromother sensors.

In embodiments, methods and systems include a monitoring system for datacollection in an industrial environment. The system includes a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors. The system includes a datastorage circuit structured to store sensor specifications, anticipatedstate information and detected values and a signal evaluation circuitcomprising: an overload identification circuit structured to determine asensor overload status of at least one sensor in response to theplurality of detection values and at least one of anticipated stateinformation and sensor specification. The system also includes a sensorfault detection circuit structured to determine one of a sensor faultstatus and a sensor validity status of at least one sensor in responseto the plurality of detection values and at least one of anticipatedstate information and sensor specification; and a response circuitstructured to perform at least one operation in response to one of asensor overload status, a sensor health status, and a sensor validitystatus. In embodiments, the system further comprising a mobile datacollector for collecting data from the plurality of input sensors. Inembodiments, the at least one operation comprises issuing an alert or analarm. In embodiments, the at least one operation further comprisesstoring additional data in the data storage circuit. In embodiments, thesystem further includes a multiplexor (MUX) circuit.

In embodiments, the at least one operation comprises at least one ofenabling or disabling one or more portions of the multiplexer circuitand altering the multiplexer control lines. In embodiments, the systemincludes at least two multiplexer (MUX) circuits and the at least oneoperation comprises changing connections between the at least twomultiplexer circuits. In embodiments, the system includes a MUX controlcircuit structured to interpret a subset of the plurality of detectionvalues and provide the logical control of the MUX and the correspondenceof MUX input and detected values as a result. In embodiments, the logiccontrol of the MUX comprises adaptive scheduling of the multiplexercontrol lines. In embodiments, methods and system include a system fordata collection, processing, and component analysis in an industrialenvironment. An example system includes a plurality of monitoringdevices, each monitoring device comprising: a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of aplurality of input sensors. The system includes a data storage forstoring specifications and anticipated state information for a pluralityof sensor types and buffering the plurality of detection values for apredetermined length of time and a signal evaluation circuit comprising:an overload identification circuit structured to determine a sensoroverload status of at least one sensor in response to the plurality ofdetection values and at least one of anticipated state information andsensor specification. The system includes a sensor fault detectioncircuit structured to determine one of a sensor fault status and asensor validity status of at least one sensor in response to theplurality of detection values and at least one of anticipated stateinformation and sensor specification; and a response circuit structuredto perform at least one operation in response to one of a sensoroverload status, a sensor health status, and a sensor validity status.The system also includes a communication circuit structured tocommunicate with a remote server providing one of the sensor overloadstatus, the sensor health status, and the sensor validity status and aportion of the buffered detection values to the remote server. Thesystem also includes a monitoring application on the remote serverstructured to: receive the at least one selected detection value and oneof the sensor overload status, the sensor health status, and the sensorvalidity status; jointly analyze a subset of the detection valuesreceived from the plurality of monitoring devices; and recommend anaction. In embodiments, at least one of the monitoring devices includesa mobile data collector for collecting data from the plurality of inputsensors. In embodiments, the at least one operation comprises issuing analert or an alarm. In embodiments, the at least one operation furthercomprises storing additional data in the data storage circuit. Inembodiments, at least one of the monitoring devices includes amultiplexor (MUX) circuit. In embodiments, the at least one operationcomprises at least one of enabling or disabling one or more portions ofthe multiplexer circuit and altering the multiplexer control lines. Inembodiments, at least one of the monitoring devices includes at leasttwo multiplexer (MUX) circuits and the at least one operation compriseschanging connections between the at least two multiplexer circuits. Inembodiments, the system includes a MUX control circuit structured tointerpret a subset of the plurality of detection values and provide thelogical control of the MUX and the correspondence of MUX input anddetected values as a result. In embodiments, the logic control of theMUX comprises adaptive scheduling of the multiplexer control lines. Inembodiments, the monitoring application comprises a remote learningcircuit structured to analyze sensor status data together sensor dataand identify correlations between sensor overload and data from othersystems. In embodiments, the monitoring application structured to subsetdetection values based on one of the sensor overload status, the sensorhealth status, the sensor validity status, the anticipated life of asensor associated with detection values, the anticipated type of theequipment associated with detection values, and operational conditionsunder which detection values were measured. In embodiments, thesupplemental information comprises one of sensor specification, sensorhistoric performance, maintenance records, repair records and ananticipated state model. In embodiments, the analysis of the subset ofdetection values comprises feeding a neural net with the subset ofdetection values and supplemental information to learn to recognizevarious sensor operating states, health states, life expectancies andfault states utilizing deep learning techniques.

Referring to FIGS. 109 through 136, embodiments of the presentdisclosure, including those involving expert systems, self-organization,machine learning, artificial intelligence, and the like, may benefitfrom the use of a neural net, such as a neural net trained for patternrecognition, for classification of one or more parameters,characteristics, or phenomena, for support of autonomous control, andother purposes. References to a neural net throughout this disclosureshould be understood to encompass a wide range of different types ofneural networks, machine learning systems, artificial intelligencesystems, and the like, such as feed forward neural networks, radialbasis function neural networks, self-organizing neural networks (e.g.,Kohonen self-organizing neural networks), recurrent neural networks,modular neural networks, artificial neural networks, physical neuralnetworks, multi-layered neural networks, convolutional neural networks,hybrids of neural networks with other expert systems (e.g., hybrid fuzzylogic—neural network systems), autoencoder neural networks,probabilistic neural networks, time delay neural networks, convolutionalneural networks, regulatory feedback neural networks, radial basisfunction neural networks, recurrent neural networks, Hopfield neuralnetworks, Boltzmann machine neural networks, self-organizing map (SOM)neural networks, learning vector quantization (LVQ) neural networks,fully recurrent neural networks, simple recurrent neural networks, echostate neural networks, long short-term memory neural networks,bi-directional neural networks, hierarchical neural networks, stochasticneural networks, genetic scale RNN neural networks, committee ofmachines neural networks, associative neural networks, physical neuralnetworks, instantaneously trained neural networks, spiking neuralnetworks, neocognition neural networks, dynamic neural networks,cascading neural networks, neuro-fuzzy neural networks, compositionalpattern-producing neural networks, memory neural networks, hierarchicaltemporal memory neural networks, deep feed forward neural networks,gated recurrent unit (GCU) neural networks, auto encoder neuralnetworks, variational auto encoder neural networks, de-noising autoencoder neural networks, sparse auto-encoder neural networks, Markovchain neural networks, restricted Boltzmann machine neural networks,deep belief neural networks, deep convolutional neural networks,deconvolutional neural networks, deep convolutional inverse graphicsneural networks, generative adversarial neural networks, liquid statemachine neural networks, extreme learning machine neural networks, echostate neural networks, deep residual neural networks, support vectormachine neural networks, neural Turing machine neural networks, and/orholographic associative memory neural networks, or hybrids orcombinations of the foregoing, or combinations with other expertsystems, such as rule-based systems, model-based systems (including onesbased on physical models, statistical models, flow-based models,biological models, biomimetic models, and the like).

In embodiments, the foregoing neural network may be configured toconnect with a DAQ instrument and other data collectors that may receiveanalog signals from one or more sensors. The foregoing neural networksmay also be configured to interface with, connect to, or integrate withexpert systems that can be local and/or available through one or morecloud networks. In embodiments, FIGS. 110 through 136 depict exemplaryneural networks and FIG. 109 depicts a legend showing the variouscomponents of the neural networks depicted throughout FIGS. 110 to 136.FIG. 109 depicts the various neural net components 10000, as depicted incells 10002 for which there are assigned functions and requirements. Inembodiments, the various neural net examples may include back feddata/sensor cells 10010, data/sensor cells 10012, noisy input cells,10014, and hidden cells, 10018. The neural net components 10000 alsoinclude the other following cells 10002: probabilistic hidden cells10020, spiking hidden cells 10022, output cells 10024, matchinput/output cell 10028, recurrent cell 10030, memory cell, 10032,different memory cell 10034, kernels 10038 and convolution or pool cells10040.

In FIG. 110, a streaming data collection system 10050 may include a DAQinstrument 10052 or other data collectors that may gather analog signalsfrom sensors including sensor 10060, sensor 10062 and sensor 10064. Thestreaming data collection system 10050 may include a perceptron neuralnetwork 10070 that may connect to, integrate with, or interface with anexpert system 10080. In FIG. 111, a streaming data collection system10090 may include the DAQ instrument 10052 or other data collectors thatmay gather analog signals from sensors including the sensors 10060,10062, 10064. The streaming data collection system 10090 may include afeed forward neural network 10092 that may connect to, integrate with,or interface with the expert system 10080. In FIG. 112, a streaming datacollection system 10100 may include the DAQ instrument 10052 or otherdata collectors that may gather analog signals from sensors includingthe sensors 10060, 10062, 10064. The streaming data collection system10100 may include a radial basis neural network 10102 that may connectto, integrate with, or interface with the expert system 10080. In FIG.113, a streaming data collection system 10110 may include the DAQinstrument 10052 or other data collectors that may gather analog signalsfrom sensors including the sensors 10060, 10062, 10064. The streamingdata collection system 10110 may include a deep feed forward neuralnetwork 10112 that may connect to, integrate with, or interface with theexpert system 10080. In FIG. 114, a streaming data collection system10120 may include the DAQ instrument 10052 or other data collectors thatmay gather analog signals from sensors including the sensors 10060,10062, 10064. The streaming data collection system 10120 may include arecurrent neural network 10122 that may connect to, integrate with, orinterface with the expert system 10080.

In FIG. 115, a streaming data collection system 10130 may include theDAQ instrument 10052 or other data collectors that may gather analogsignals from sensors including the sensors 10060, 10062, 10064. Thestreaming data collection system 10130 may include a long/short termneural network 10132 that may connect to, integrate with, or interfacewith the expert system 10080. In FIG. 116, a streaming data collectionsystem 10140 may include the DAQ instrument 10052 or other datacollectors that may gather analog signals from sensors including thesensors 10060, 10062, 10064. The streaming data collection system 10140may include a gated recurrent neural network 10142 that may connect to,integrate with, or interface with the expert system 10080. In FIG. 117,a streaming data collection system 10150 may include the DAQ instrument10052 or other data collectors that may gather analog signals fromsensors including the sensors 10060, 10062, 10064. The streaming datacollection system 10150 may include an auto encoder neural network 10152that may connect to, integrate with, or interface with the expert system10080. In FIG. 118, a streaming data collection system 10160 may includethe DAQ instrument 10052 or other data collectors that may gather analogsignals from sensors including the sensors 10060, 10062, 10064. Thestreaming data collection system 10160 may include a variational neuralnetwork 10162 that may connect to, integrate with, or interface with theexpert system 10080. In FIG. 119, a streaming data collection system10170 may include the DAQ instrument 10052 or other data collectors thatmay gather analog signals from sensors including the sensors 10060,10062, 10064. The streaming data collection system 10170 may include adenoising neural network 10172 that may connect to, integrate with, orinterface with the expert system 10080. In FIG. 120, a streaming datacollection system 10180 may include the DAQ instrument 10052 or otherdata collectors that may gather analog signals from sensors includingthe sensors 10060, 10062, 10064. The streaming data collection system10180 may include a sparse neural network 10182 that may connect to,integrate with, or interface with the expert system 10080. In FIG. 121,a streaming data collection system 10190 may include the DAQ instrument10052 or other data collectors that may gather analog signals fromsensors including the sensors 10060, 10062, 10064. The streaming datacollection system 10190 may include a Markov chain neural network 10182that may connect to, integrate with, or interface with the expert system10080.

In FIG. 122, a streaming data collection system 10200 may include theDAQ instrument 10052 or other data collectors that may gather analogsignals from sensors including the sensors 10060, 10062, 10064. Thestreaming data collection system 10200 may include a Hopfield networkneural network 10202 that may connect to, integrate with, or interfacewith the expert system 10080. In FIG. 123, a streaming data collectionsystem 10210 may include the DAQ instrument 10052 or other datacollectors that may gather analog signals from sensors including thesensors 10060, 10062, 10064. The streaming data collection system 10210may include a Boltzmann machine neural network 10212 that may connectto, integrate with, or interface with the expert system 10080. In FIG.124, a streaming data collection system 10220 may include the DAQinstrument 10052 or other data collectors that may gather analog signalsfrom sensors including the sensors 10060, 10062, 10064. The streamingdata collection system 10220 may include a restricted BM neural network10222 that may connect to, integrate with, or interface with the expertsystem 10080. In FIG. 125, a streaming data collection system 10230 mayinclude the DAQ instrument 10052 or other data collectors that maygather analog signals from sensors including the sensors 10060, 10062,10064. The streaming data collection system 10230 may include a deepbelief neural network 10232 that may connect to, integrate with, orinterface with the expert system 10080. In FIG. 126, a streaming datacollection system 10240 may include the DAQ instrument 10052 or otherdata collectors that may gather analog signals from sensors includingthe sensors 10060, 10062, 10064. The streaming data collection system10240 may include a deep convolutional neural network 10242 that mayconnect to, integrate with, or interface with the expert system 10080.In FIG. 127, a streaming data collection system 10250 may include theDAQ instrument 10052 or other data collectors that may gather analogsignals from sensors including the sensors 10060, 10062, 10064. Thestreaming data collection system 10250 may include the deconvolutionalneural network 10242 that may connect to, integrate with, or interfacewith the expert system 10080. In FIG. 128, the streaming data collectionsystem 10260 may include the DAQ instrument 10052 or other datacollectors that may gather analog signals from sensors including thesensors 10060, 10062, 10064. The streaming data collection system 10260may include a deep convolutional inverse graphics neural network 10262that may connect to, integrate with, or interface with the expert system10080. In FIG. 129, a streaming data collection system 10270 may includethe DAQ instrument 10052 or other data collectors that may gather analogsignals from sensors including the sensors 10060, 10062, 10064. Thestreaming data collection system 10270 may include a generativeadversarial neural network 10272 that may connect to, integrate with, orinterface with the expert system 10080. In FIG. 130, a streaming datacollection system 10280 may include the DAQ instrument 10052 or otherdata collectors that may gather analog signals from sensors includingthe sensors 10060, 10062, 10064. The streaming data collection system10280 may include a liquid state machine neural network 10282 that mayconnect to, integrate with, or interface with the expert system 10080.In FIG. 131, a streaming data collection system 10290 may include theDAQ instrument 10052 or other data collectors that may gather analogsignals from sensors including the sensors 10060, 10062, 10064. Thestreaming data collection system 10290 may include an extreme learningmachine neural network 10292 that may connect to, integrate with, orinterface with the expert system 10080. In FIG. 132, a streaming datacollection system 10300 may include the DAQ instrument 10052 or otherdata collectors that may gather analog signals from sensors includingthe sensors 10060, 10062, 10064. The streaming data collection system10300 may include an echo state neural network 10302 that may connectto, integrate with, or interface with the expert system 10080. In FIG.133, a streaming data collection system 10310 may include the DAQinstrument 10052 or other data collectors that may gather analog signalsfrom sensors including the sensors 10060, 10062, 10064. The streamingdata collection system 10310 may include a deep residual neural network10312 that may connect to, integrate with, or interface with the expertsystem 10080. In FIG. 134, a streaming data collection system 10320 mayinclude the DAQ instrument 10052 or other data collectors that maygather analog signals from sensors including the sensors 10060, 10062,10064. The streaming data collection system 10320 may include a Kohonenneural network 10322 that may connect to, integrate with, or interfacewith the expert system 10080. In FIG. 135, a streaming data collectionsystem 10330 may include the DAQ instrument 10052 or other datacollectors that may gather analog signals from sensors including thesensors 10060, 10062, 10064. The streaming data collection system 10330may include a support vector machine neural network 10332 that mayconnect to, integrate with, or interface with the expert system 10080.In FIG. 136, a streaming data collection system 10340 may include theDAQ instrument 10052 or other data collectors that may gather analogsignals from sensors including the sensors 10060, 10062, 10064. Thestreaming data collection system 10340 may include a neural Turingmachine neural network 10342 that may connect to, integrate with, orinterface with the expert system 10080.

In embodiments, industrial machine sensor data streaming actions, suchas those associated with a streaming data collector as described hereinmay include data collection processes that facilitate collection of dataabout the industrial machine with zero or nearly zero gaps for samplingrates that meet a minimum rate value. Data about the industrial machinemay be collected from, for example without limitation, sensors deployedwith the industrial machine to sense at least one aspect of the machine,such as operational rates of moving parts, vibration rates of structuraland other parts, and the like. A data collection process that performsdata collection at a minimum sampling rate with zero or near-zero gapsin an industrial machine environment may involve configuring datacollection routing resources within and across industrial machines andthe like to accommodate uninterrupted data collection at the samplingrate. In embodiments, configuring data collection routing resources forzero-gap performance may include configuring a data collector that isphysically close to the data collectors (e.g., sensors) with enoughmemory storage to store the required amount of data at the requiredsampling rate. In embodiments, configuring data collection routingresources for zero-gap performance may include configuring datacollection devices (e.g., sensors and analog-to-digital converters, andthe like), configuring data routing resources, such as networkingdevices, network switching devices (e.g., data routers, multiplexers,and the like), configuring data storage devices and the like to ensurethat all data sampled at the minimum sampling rate is captured andstored for future reference, such as by data analysis algorithms and thelike.

In embodiments, zero-gap data collection processes may involvecollecting data in an uninterrupted fashion at sufficiently fastsampling rate, which may be a multiple of the Nyquist frequency and thelike, with resolution (e.g., number of bits per sample) and length(e.g., number of samples) to support producing at least one unprocessedoutput stream of sampled data that is stored in a computer accessibledata storage facility. In embodiments, such data collection processesmay further include handling processed streams (e.g., processing thesampled data into an additional output stream for purposes such asstorage, display, evaluation, exchange, communication with expertsystems, statistical analyzers and the like. The methods and systems ofdata collecting, data routing and the like as described herein may relyon configurations of data collection, routing, processing, storage,communication, and display industrial machine-related resources.

In embodiments, performing data collection to support both unprocessedand processed output data streams may be accomplished by performing asingle sampling process at a sufficiently high sampling rate toaccommodate all processing needs. Rather than performing sequential datacollection events at a plurality of frequencies each needed for aspecific purpose (e.g., storage, display, statistical analysis and thelike), a single high frequency sampling rate may be selected. Theresulting stream of sensor data may be routed to industrial machine datahandling resources that may perform functions, such as down-sampling,raw data storage, statistical and other analysis and the like. Inexamples, a raw sensor data sampling rate may be 40 kHz, 100 kHz orgreater (referred to herein as “streaming rate”). A sampling inputstream may be defined in terms of sample resolution and length at thisraw sensor data sampling rate to accomplish desired analysis. This rawsensor data may be stored in a computer accessible memory. Processing ofthe data may occur only when and if needed by merely accessing thestored sensor data and processing it as needed. In embodiments, inaddition to storing the raw sensor data, during the sampling process,such as at streaming rate, the raw data may be routed to and processedby data analysis (e.g., statistical) resources at any rate less than thestreaming rate. In an example, the streaming rate raw sampled data maybe routed to a data analysis facility that converts the data into alower frequency representation (e.g., 500 Hz) and performs certain dataanalysis, display and the like operations for which 500 Hz sample rateis acceptable. Likewise, the streaming rate input sensor data stream maybe routed to additional data analysis resources that perform dataanalysis at 5 kHz by first down sampling the raw data to make the resultcomparable to 5 kHz sampling. By performing a single data collectionevent at the higher rate of, for example the streaming rate and thelike, all data needed from the sensors for a wide range of analysis,display, and reference operations—essentially anything that requires asample rate of the streaming rate or less, can be captured and provideddirectly (e.g., in multiple output data streams) to the processingfacilities as well as stored for later indirect processing, and thelike. Such an approach reduces impact on an operating machine, frees updata collection and routing bandwidth for other types of sensing androutine, simplifies configuring data collection facilities due to use ofa common sampling rate, and the like.

In embodiments, zero-gap signal capture at a streaming sample rate mayinclude sampling a signal at the streaming sample rate, therebyproducing a plurality of samples of the signal. The plurality of samplesof the signal may be allocated with a signal routing circuit thatgenerates a first portion of the plurality of samples of the signal to afirst signal analysis circuit, the portion based on a first signalanalysis sampling rate that is less than the streaming sample rate. Theplurality of samples of the signal may be allocated with a signalrouting circuit that generates a second portion of the plurality ofsamples of the signal to a second signal analysis circuit, the portionbased on a second signal analysis sampling rate that is less than thestreaming sample rate. In embodiments, the zero-gap signal capture mayfurther include storing the plurality of samples of the signal, anoutput of the first signal analysis circuit, and an output of the secondsignal analysis circuit. In embodiments, the allocated first portion andthe second portion of the plurality of samples in the stored pluralityof samples are tagged with indicia that references the correspondingstored signal analysis output.

In embodiments, allocating with the signal routing circuit comprisesintegrating a plurality of samples based on a ratio of the signalanalysis sampling rate and the streaming sample rate. Allocating mayalso include selecting samples of the signal based on a ratio of thesignal analysis sampling rate and the streaming sample rate. Inembodiments, the streaming sample rate is at least twice as fast as adominant frequency of the signal.

In embodiments, the ratio of the signal analysis sampling rate to thestreaming sample rate determines a number of supplemental binary bits ofdata of the output of the first and second signal analysis circuits. Thenumber of supplemental binary bits may be one when the streaming samplerate is at least twice and less than four times the signal analysissampling rate. It may be two when the streaming sample rate is at leastfour times and less than eight times the signal analysis sampling rate.

In embodiments, a system that facilitates zero-gap signal sensing of acondition of an industrial machine may include a sensor detecting acondition of an industrial machine, the sensor producing a signal thatvaries over time and substantially corresponds with the condition. Thesystem may also include an analog to digital converter that receives thesignal and samples the signal at a streaming sample rate that is atleast twice a dominant frequency of the signal, the sampled signal beingoutput from the analog to digital converter as a sequence of datavalues. The system may further include at least one digital signalrouter that receives the sequence of data value and a sub-sampling rate.In embodiments, the sub-sampling rate is lower than the streaming samplerate, and produces at least one sub-sampled output sequence of datacomprising select samples from the sequence of samples based on at leastone of the sub-sampling rate and a ratio of the streaming sample rateand the sub-sampling rate. In embodiments, the system may also include adata storage facility that receives the sequence of data values and ananalyzed set of data values derived from the sub-sampled outputsequence. In embodiments, the analyzed set of data values are stored inassociation with the sequence of data values such that data values inthe sequence of data values that correspond to the sub-sampled outputsequence are tagged with indicia that references the correspondinganalyzed set of data values.

In embodiments, producing the at least one sub-sampled output sequencemay include integrating a plurality of samples in the sequence of datavalues based on a ratio of the sub-sampling rate and the streamingsample rate. Also, producing the at least one sub-sampled outputsequence may include selecting samples of the signal based on a ratio ofthe sub-sampling rate and the streaming sample rate. In embodiments, thestreaming sample rate is at least twice as fast as a dominant frequencyof the signal.

In embodiments, the ratio of the sub-sampling rate to the streamingsample rate determines a number of supplemental binary bits in thesub-sampled output sequence. In embodiments, the number of supplementalbinary bits is one when the streaming sample rate is at least twice andless than four times the sub-sampling rate and it is two bits when thestreaming sample rate is at least four times and less than eight timesthe sub-sampling rate.

In embodiments, in addition to providing simultaneous logical access toa plurality of lower sampling rates when using a high sampling rate,such as the streaming rate or the like, for signals being sampled thathave an inherent frequency much lower than the sampling rate, theresulting oversampling effectively provides additional resolution of thesampled signal in the collected data set. As the rate of oversamplingincreases relative to the sampled signal's frequency, so does theeffective resolution. In embodiments, for a typical 16-bit per sampledata collector, oversampling by a factor of 2 results in an additionalbit of effective signal sampled resolution, thereby effectivelyproviding 17 bits of signal information per sample. Oversampling by afactor of 4 results in an additional 2 bits of resolution per sample.This general rule of increasing effective resolution by one bit for eachdoubling of the sampling rate can be represented by a functionalalgorithm: n=Log(2)(O), where O is the oversampling factor (1, 2, 4, andthe like) and “n” is the effective increase in resolution as a count ofadditional bits of resolution effectively captured with each sample.

In embodiments, oversampling further benefits accurately capturing lowfrequency components while mitigating an impact of sample-induced andanalysis-induced noise in the signal data. Streaming data at a highsampling rate (per channel) such as the streaming rate typically permitshigh oversampling ratios to be used to achieve high-resolution lowerfrequency data, such as for example approximately 1× running speedvibration peaks, the associated lower order harmonics of these and otherlower frequency peaks. This may provide the added benefit of increasedamplitude resolution as described above, namely that the increase inoversampling by a factor of N increases the waveform resolution by thesame factor that are represented by an increase in the number of binarybits for each sample. The # of additional binary bits may be calculatedas: Nb=LOG(2)(N). In embodiments, signal processing techniques such asDigital Integration that creates low frequency 1/f noise (ski-slope) maybenefit from the effective increased resolution because the noise issignificantly reduced by the increased resolution benefit ofoversampling. Because streaming data is most commonly collected withaccelerometers, integrating the accelerometer data is often done toconvert the raw accelerometer data to units that more directly relate toseverity. In embodiments, oversampling as described herein may be aviable and practical alternative, such as for situations where theanalog noise in the signal being measured falls below the effective A/Dresolution (e.g., standard A/D resolution of hardware enhanced byoversampling).

In embodiments, an analog to digital converter may have a resolution of16 bits or +/−15 bits to measure a +/−voltage waveform. Such anembodiment provides −90 dB of resolution when compared to the fullscale. In an example, the measured signal may be 5 volt peak to peak.Without oversampling, such a sampling function would be expected toproduce an amplitude resolution of approximately +/−153 microvolt(1.526×10{circumflex over ( )}−4 volt). However, with oversampling onecan expect an improvement of up to −23 dB or more, thereby increasingthe resolution further to approximately −113 dB. When signal amplitudeprocessing is employed, such as to reduce the full-scale voltage from 5volts to, for example 10 mV, the resolution may be increased further by5 volts/10 mV=500=54 dB (approx.). Further processing, such as bynormalizing to 1 volt to facilitate compare with the 5 volts full scalesignal: 1 volt/10 mV=40 dB. Thus, this improvement is additive to theprevious oversampled resolution of −113−40 to −153 dB. This effectivelyresults in a resolution of 2.2×10{circumflex over ( )}−8=22 nanovolts.

In embodiments, application of the above concepts to coherent signalsproduces the indicated improvement in resolution and the like. However,noise components of signals may be rendered in the power spectrum tofacilitate determining an impact on measurement resolution of the noiseas described.

In embodiments, techniques for improving resolution through oversamplingmay be performed in hardware and a combination of hardware and software.The algorithms presented may be encoded in hardware components, such ascustom, semi-custom, and programmable array hardware devices and thelike. The algorithms presented may alternatively be executed bydedicated processing resources, multi-purpose computing devices, and thelike. In embodiments, some portion(s) of the algorithms may be performedin hardware (e.g., sampling, voltage reduction, and the like) and otherportions with a processor (e.g., log function and the like). Hardwareprocessing may be preferred for certain environments, such as when thenoise (e.g., electronically-induced) prevents separate of the signalfrom the noise for the desired degree of resolution. In embodiments,hardware processing may be improved through at least one of thefollowing two techniques: (i) multi-channel data integration; and (ii)use of hardware filtering. In embodiments, use of a multi-channel dataintegrator may include collecting data from at least 2 channels, onechannel with hardware integration, such as for low-frequency portions ofa sampled signal and the other channel without hardware integration suchas for high-frequency portions of a sampled signal. In embodiments, useof hardware filtering may include splicing integrated data from onechannel that has been Low-Pass filtered with non-integrated data thathas been high-pass filtered so that the filters have overlapping filterrolloff regions that can be used to help splice the data together.

The foregoing neural networks may have a variety of nodes or neurons,which may perform a variety of functions on inputs, such as inputsreceived from sensors or other data sources, including other nodes.Functions may involve weights, features, feature vectors, and the like.Neurons may include perceptrons, neurons that mimic biological functions(such as of the human senses of touch, vision, taste, hearing, andsmell), and the like. Continuous neurons, such as with sigmoidalactivation, may be used in the context of various forms of neural net,such as where back propagation is involved.

In many embodiments, an expert system or neural network may be trained,such as by a human operator or supervisor, or based on a data set,model, or the like. Training may include presenting the neural networkwith one or more training data sets that represent values, such assensor data, event data, parameter data, and other types of data(including the many types described throughout this disclosure), as wellas one or more indicators of an outcome, such as an outcome of aprocess, an outcome of a calculation, an outcome of an event, an outcomeof an activity, or the like. Training may include training inoptimization, such as training a neural network to optimize one or moresystems based on one or more optimization approaches, such as Bayesianapproaches, parametric Bayes classifier approaches, k-nearest-neighborclassifier approaches, iterative approaches, interpolation approaches,Pareto optimization approaches, algorithmic approaches, and the like.Feedback may be provided in a process of variation and selection, suchas with a genetic algorithm that evolves one or more solutions based onfeedback through a series of rounds.

In embodiments, a plurality of neural networks may be deployed in acloud platform that receives data streams and other inputs collected(such as by mobile data collectors) in one or more industrialenvironments and transmitted to the cloud platform over one or morenetworks, including using network coding to provide efficienttransmission. In the cloud platform, optionally using massively parallelcomputational capability, a plurality of different neural networks ofseveral types (including modular forms, structure-adaptive forms,hybrids, and the like) may be used to undertake prediction,classification, control functions, and provide other outputs asdescribed in connection with expert systems disclosed throughout thisdisclosure. The different neural networks may be structured to competewith each other (optionally including the use of evolutionaryalgorithms, genetic algorithms, or the like), such that an appropriatetype of neural network, with appropriate input sets, weights, node typesand functions, and the like, may be selected, such as by an expertsystem, for a specific task involved in a given context, workflow,environment process, system, or the like.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed forwardneural network, which moves information in one direction, such as from adata input, like an analog sensor located on or proximal to anindustrial machine, through a series of neurons or nodes, to an output.Data may move from the input nodes to the output nodes, optionallypassing through one or more hidden nodes, without loops. In embodiments,feedforward neural networks may be constructed with various types ofunits, such as binary McCulloch-Pitts neurons, the simplest of which isa perceptron.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, which may be preferred in some situationsinvolving interpolation in a multi-dimensional space (such as whereinterpolation is helpful in optimizing a multi-dimensional function,such as for optimizing a data marketplace as described here, optimizingthe efficiency or output of a power generation system, a factory system,or the like, or other situation involving multiple dimensions). Inembodiments, each neuron in the RBF neural network stores an examplefrom a training set as a “prototype.” Linearity involved in thefunctioning of this neural network offers RBF the advantage of nottypically suffering from problems with local minima or maxima.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, such as one that employs a distancecriterion with respect to a center (e.g., a Gaussian function). A radialbasis function may be applied as a replacement for a hidden layer (suchas a sigmoidal hidden layer transfer) in a multi-layer perceptron. AnRBF network may have two layers, such as the case where an input ismapped onto each RBF in a hidden layer. In embodiments, an output layermay comprise a linear combination of hidden layer values representing,for example, a mean predicted output. The output layer value may providean output that is the same as or similar to that of a regression modelin statistics. In classification problems, the output layer may be asigmoid function of a linear combination of hidden layer values,representing a posterior probability. Performance in both cases is oftenimproved by shrinkage techniques, such as ridge regression in classicalstatistics. This corresponds to a prior belief in small parameter values(and therefore smooth output functions) in a Bayesian framework. RBFnetworks may avoid local minima, because the only parameters that areadjusted in the learning process are the linear mapping from hiddenlayer to output layer. Linearity ensures that the error surface isquadratic and therefore has a single minimum. In regression problems,this can be found in one matrix operation. In classification problems,the fixed non-linearity introduced by the sigmoid output function may behandled using an iteratively re-weighted least squares function or thelike.

RBF networks may use kernel methods such as support vector machines(SVM) and Gaussian processes (where the RBF is the kernel function). Anon-linear kernel function may be used to project the input data into aspace where the learning problem can be solved using a linear model.

In embodiments, an RBF neural network may include an input layer, ahidden layer, and a summation layer. In the input layer, one neuronappears in the input layer for each predictor variable. In the case ofcategorical variables, N−1 neurons are used, where N is the number ofcategories. The input neurons may, in embodiments, standardize the valueranges by subtracting the median and dividing by the interquartilerange. The input neurons may then feed the values to each of the neuronsin the hidden layer. In the hidden layer, a variable number of neuronsmay be used (determined by the training process). Each neuron mayconsist of a radial basis function that is centered on a point with asmany dimensions as a number of predictor variables. The spread (e.g.,radius) of the RBF function may be different for each dimension. Thecenters and spreads may be determined by training. When presented with avector of input values from the input layer, a hidden neuron may computea Euclidean distance of the test case from the neuron's center point andthen apply the RBF kernel function to this distance, such as using thespread values. The resulting value may then be passed to the summationlayer. In the summation layer, the value coming out of a neuron in thehidden layer may be multiplied by a weight associated with the neuronand may add to the weighted values of other neurons. This sum becomesthe output. For classification problems, one output is produced (with aseparate set of weights and summation units) for each target category.The value output for a category is the probability that the case beingevaluated has that category. In training of an RBF, various parametersmay be determined, such as the number of neurons in a hidden layer, thecoordinates of the center of each hidden-layer function, the spread ofeach function in each dimension, and the weights applied to outputs asthey pass to the summation layer. Training may be used by clusteringalgorithms (such as k-means clustering), by evolutionary approaches, andthe like.

In embodiments, a recurrent neural network may have a time-varying,real-valued (more than just zero or one) activation (output). Eachconnection may have a modifiable real-valued weight. Some of the nodesare called labeled nodes, some output nodes, and others hidden nodes.For supervised learning in discrete time settings, training sequences ofreal-valued input vectors may become sequences of activations of theinput nodes, one input vector at a time. At each time step, eachnon-input unit may compute its current activation as a nonlinearfunction of the weighted sum of the activations of all units from whichit receives connections. The system can explicitly activate (independentof incoming signals) some output units at certain time steps.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingneural network, such as a Kohonen self-organizing neural network, suchas for visualization of views of data, such as low-dimensional views ofhigh-dimensional data. The self-organizing neural network may applycompetitive learning to a set of input data, such as from one or moresensors or other data inputs from or associated with an industrialmachine. In embodiments, the self-organizing neural network may be usedto identify structures in data, such as unlabeled data, such as in datasensed from a range of vibration, acoustic, or other analog sensors inan industrial environment, where sources of the data are unknown (suchas where vibrations may be coming from any of a range of unknownsources). The self-organizing neural network may organize structures orpatterns in the data, such that they can be recognized, analyzed, andlabeled, such as identifying structures as corresponding to vibrationsinduced by the movement of a floor, or acoustic signals created by highfrequency rotation of a shaft of a somewhat distant machine.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a recurrent neuralnetwork, which may allow for a bi-directional flow of data, such aswhere connected units (e.g., neurons or nodes) form a directed cycle.Such a network may be used to model or exhibit dynamic temporalbehavior, such as those involved in dynamic systems including a widevariety of the industrial machines and devices described throughout thisdisclosure, such as a power generation machine operating at variablespeeds or frequencies in variable conditions with variable inputs, arobotic manufacturing system, a refining system, or the like, wheredynamic system behavior involves complex interactions that an operatormay desire to understand, predict, control and/or optimize. For example,the recurrent neural network may be used to anticipate the state (suchas a maintenance state, a fault state, an operational state, or thelike), of an industrial machine, such as one performing a dynamicprocess or action. In embodiments, the recurrent neural network may useinternal memory to process a sequence of inputs, such as from othernodes and/or from sensors and other data inputs from the industrialenvironment, of the various types described herein. In embodiments, therecurrent neural network may also be used for pattern recognition, suchas for recognizing an industrial machine based on a sound signature, aheat signature, a set of feature vectors in an image, a chemicalsignature, or the like. In a non-limiting example, a recurrent neuralnetwork may recognize a shift in an operational mode of a turbine, agenerator, a motor, a compressor, or the like (such as a gear shift) bylearning to classify the shift from a training data set consisting of astream of data from tri-axial vibration sensors and/or acoustic sensorsapplied to one or more of such machines.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a modular neuralnetwork, which may comprise a series of independent neural networks(such as ones of various types described herein) that are moderated byan intermediary. Each of the independent neural networks in the modularneural network may work with separate inputs, accomplishing subtasksthat make up the task the modular network as whole is intended toperform. For example, a modular neural network may comprise a recurrentneural network for pattern recognition, such as to recognize what typeof industrial machine is being sensed by one or more sensors that areprovided as input channels to the modular network and an RBF neuralnetwork for optimizing the behavior of the machine once understood. Theintermediary may accept inputs of each of the individual neuralnetworks, process them, and create output for the modular neuralnetwork, such an appropriate control parameter, a prediction of state,or the like.

Combinations among any of the pairs, triplets, or larger combinations,of the various neural network types described herein, are encompassed bythe present disclosure. This may include combinations where an expertsystem uses one neural network for recognizing a pattern (e.g., apattern indicating a problem or fault condition) and a different neuralnetwork for self-organizing an activity or work flow based on therecognized pattern (such as providing an output governing autonomouscontrol of a system in response to the recognized condition or pattern).This may also include combinations where an expert system uses oneneural network for classifying an item (e.g., identifying a machine, acomponent, or an operational mode) and a different neural network forpredicting a state of the item (e.g., a fault state, an operationalstate, an anticipated state, a maintenance state, or the like). Modularneural networks may also include situations where an expert system usesone neural network for determining a state or context (such as a stateof a machine, a process, a work flow, a marketplace, a storage system, anetwork, a data collector, or the like) and a different neural networkfor self-organizing a process involving the state or context (e.g., adata storage process, a network coding process, a network selectionprocess, a data marketplace process, a power generation process, amanufacturing process, a refining process, a digging process, a boringprocess, or other process described herein).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a physical neuralnetwork where one or more hardware elements is used to perform orsimulate neural behavior. In embodiments, one or more hardware neuronsmay be configured to stream voltage values that represent analogvibration sensor data voltage values, to calculate velocity informationfrom analog sensor inputs representing acoustic, vibration or otherdata, to calculation acceleration information from sensor inputsrepresenting acoustic, vibration, or other data, or the like. One ormore hardware nodes may be configured to stream output data resultingfrom the activity of the neural net. Hardware nodes, which may compriseone or more chips, microprocessors, integrated circuits, programmablelogic controllers, application-specific integrated circuits,field-programmable gate arrays, or the like, may be provided to optimizethe speed, input/output efficiency, energy efficiency, signal to noiseratio, or other parameter of some part of a neural net of any of thetypes described herein. Hardware nodes may include hardware foracceleration of calculations (such as dedicated processors forperforming basic or more sophisticated calculations on input data toprovide outputs, dedicated processors for filtering or compressing data,dedicated processors for decompressing data, dedicated processors forcompression of specific file or data types (e.g., for handling imagedata, video streams, acoustic signals, vibration data, thermal images,heat maps, or the like), and the like. A physical neural network may beembodied in a data collector, such as a mobile data collector describedherein, including one that may be reconfigured by switching or routinginputs in varying configurations, such as to provide different neuralnet configurations within the data collector for handling differenttypes of inputs (with the switching and configuration optionally undercontrol of an expert system, which may include a software-based neuralnet located on the data collector or remotely). A physical, or at leastpartially physical, neural network may include physical hardware nodeslocated in a storage system, such as for storing data within anindustrial machine or in an industrial environment, such as foraccelerating input/output functions to one or more storage elements thatsupply data to or take data from the neural net. A physical, or at leastpartially physical, neural network may include physical hardware nodeslocated in a network, such as for transmitting data within, to or froman industrial environment, such as for accelerating input/outputfunctions to one or more network nodes in the net, accelerating relayfunctions, or the like. In embodiments of a physical neural network, anelectrically adjustable resistance material may be used for emulatingthe function of a neural synapse. In embodiments, the physical hardwareemulates the neurons, and software emulates the neural network betweenthe neurons. In embodiments, neural networks complement conventionalalgorithmic computers. They are versatile and can be trained to performappropriate functions without the need for any instructions, such asclassification functions, optimization functions, pattern recognitionfunctions, control functions, selection functions, evolution functions,and others.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a multilayeredfeed forward neural network, such as for complex pattern classificationof one or more items, phenomena, modes, states, or the like. Inembodiments, a multilayered feedforward neural network may be trained byan optimization technical, such as a genetic algorithm, such as toexplore a large and complex space of options to find an optimum, ornear-optimum, global solution. For example, one or more geneticalgorithms may be used to train a multilayered feedforward neuralnetwork to classify complex phenomena, such as to recognize complexoperational modes of industrial machines, such as modes involvingcomplex interactions among machines (including interference effects,resonance effects, and the like), modes involving non-linear phenomena,such as impacts of variable speed shafts, which may make analysis ofvibration and other signals difficult, modes involving critical faults,such as where multiple, simultaneous faults occur, making root causeanalysis difficult, and others. In embodiments, a multilayered feedforward neural network may be used to classify results from ultrasonicmonitoring or acoustic monitoring of an industrial machine, such asmonitoring an interior set of components within a housing, such as motorcomponents, pumps, valves, fluid handling components, and many others,such as in refrigeration systems, refining systems, reactor systems,catalytic systems, and others.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feedforward,back-propagation multi-layer perceptron (MLP) neural network, such asfor handling one or more remote sensing applications, such as for takinginputs from sensors distributed throughout various industrialenvironments. In embodiments, the MLP neural network may be used forclassification of physical environments, such as mining environments,exploration environments, drilling environments, and the like, includingclassification of geological structures (including underground featuresand above ground features), classification of materials (includingfluids, minerals, metals, and the like), and other problems. This mayinclude fuzzy classification.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use astructure-adaptive neural network, where the structure of a neuralnetwork is adapted, such as based on a rule, a sensed condition, acontextual parameter, or the like. For example, if a neural network doesnot converge on a solution, such as classifying an item or arriving at aprediction, when acting on a set of inputs after some amount oftraining, the neural network may be modified, such as from a feedforwardneural network to a recurrent neural network, such as by switching datapaths between some subset of nodes from unidirectional to bi-directionaldata paths. The structure adaptation may occur under control of anexpert system, such as to trigger adaptation upon occurrence of atrigger, rule or event, such as recognizing occurrence of a threshold(such as an absence of a convergence to a solution within a given amountof time) or recognizing a phenomenon as requiring different oradditional structure (such as recognizing that a system is varyingdynamically or in a non-linear fashion). In one non-limiting example, anexpert system may switch from a simple neural network structure like afeedforward neural network to a more complex neural network structurelike a recurrent neural network, a convolutional neural network, or thelike upon receiving an indication that a continuously variabletransmission is being used to drive a generator, turbine, or the like ina system being analyzed.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an autoencoder,autoassociator or Diabolo neural network, which may be similar to amultilayer perceptron (“MLP”) neural network, such as where there may bean input layer, an output layer and one or more hidden layers connectingthem. However, the output layer in the auto-encoder may have the samenumber of units as the input layer, where the purpose of the MLP neuralnetwork is to reconstruct its own inputs (rather than just emitting atarget value). Therefore, the auto encoders may operate as anunsupervised learning model. An auto encoder may be used, for example,for unsupervised learning of efficient coding, such as fordimensionality reduction, for learning generative models of data, andthe like. In embodiments, an auto-encoding neural network may be used toself-learn an efficient network coding for transmission of analog sensordata from an industrial machine over one or more networks. Inembodiments, an auto-encoding neural network may be used to self-learnan efficient storage approach for storage of streams of analog sensordata from an industrial environment.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a probabilisticneural network (“PNN”), which in embodiments may comprise a multi-layer(e.g., four-layer) feedforward neural network, where layers may includeinput layers, hidden layers, pattern/summation layers and an outputlayer. In an embodiment of a PNN algorithm, a parent probabilitydistribution function (PDF) of each class may be approximated, such asby a Parzen window and/or a non-parametric function. Then, using the PDFof each class, the class probability of a new input is estimated, andBayes' rule may be employed, such as to allocate it to the class withthe highest posterior probability. A PNN may embody a Bayesian networkand may use a statistical algorithm or analytic technique, such asKernel Fisher discriminant analysis technique. The PNN may be used forclassification and pattern recognition in any of a wide range ofembodiments disclosed herein. In one non-limiting example, aprobabilistic neural network may be used to predict a fault condition ofan engine based on collection of data inputs from sensors andinstruments for the engine.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a time delayneural network (TDNN), which may comprise a feedforward architecture forsequential data that recognizes features independent of sequenceposition. In embodiments, to account for time shifts in data, delays areadded to one or more inputs, or between one or more nodes, so thatmultiple data points (from distinct points in time) are analyzedtogether. A time delay neural network may form part of a larger patternrecognition system, such as using a perceptron network. In embodiments,a TDNN may be trained with supervised learning, such as where connectionweights are trained with back propagation or under feedback. Inembodiments, a TDNN may be used to process sensor data from distinctstreams, such as a stream of velocity data, a stream of accelerationdata, a stream of temperature data, a stream of pressure data, and thelike, where time delays are used to align the data streams in time, suchas to help understand patterns that involve understanding of the variousstreams (e.g., where increases in pressure and acceleration occur as anindustrial machine overheats).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a convolutionalneural network (referred to in some cases as a CNN, a ConvNet, a shiftinvariant neural network, or a space invariant neural network). Inembodiments, the units are connected in a pattern similar to the visualcortex of the human brain. Neurons may respond to stimuli in arestricted region of space, referred to as a receptive field. Receptivefields may partially overlap, such that they collectively cover theentire (e.g., visual) field. Node responses can be calculatedmathematically, such as by a convolution operation, such as usingmultilayer perceptrons that use minimal preprocessing. A convolutionalneural network may be used for recognition within images and videostreams, such as for recognizing a type of machine in a largeenvironment using a camera system disposed on a mobile data collector,such as on a drone or mobile robot. In embodiments, a convolutionalneural network may be used to provide a recommendation based on datainputs, including sensor inputs and other contextual information, suchas recommending a route for a mobile data collector. In embodiments, aconvolutional neural network may be used for processing inputs, such asfor natural language processing of instructions provided by one or moreparties involved in a workflow in an environment. In embodiments, aconvolutional neural network may be deployed with a large number ofneurons (e.g., 100,000, 500,000 or more), with multiple (e.g., 4, 5, 6or more) layers, and with many (e.g., millions) parameters. Aconvolutional neural net may use one or more convolutional nets.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a regulatoryfeedback network, such as for recognizing emergent phenomena (such asnew types of faults not previously understood in an industrialenvironment).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingmap (“SOM”), involving unsupervised learning. A set of neurons may learnto map points in an input space to coordinates in an output space. Theinput space can have different dimensions and topology from the outputspace, and the SOM may preserve these while mapping phenomena intogroups.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a learning vectorquantization neural net (“LVQ”). Prototypical representatives of theclasses may parameterize, together with an appropriate distance measure,in a distance-based classification scheme.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an echo statenetwork (“ESN”), which may comprise a recurrent neural network with asparsely connected, random hidden layer. The weights of output neuronsmay be changed (e.g., the weights may be trained based on feedback). Inembodiments, an ESN may be used to handle time series patterns, such as,in an example, recognizing a pattern of events associated with a gearshift in an industrial turbine, generator, or the like.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a bi-directional,recurrent neural network (“BRNN”), such as using a finite sequence ofvalues (e.g., voltage values from a sensor) to predict or label eachelement of the sequence based on both the past and the future context ofthe element. This may be done by adding the outputs of two RNNs, such asone processing the sequence from left to right, the other one from rightto left. The combined outputs are the predictions of target signals,such as those provided by a teacher or supervisor. A bi-directional RNNmay be combined with a long short-term memory RNN.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchical RNNthat connects elements in various ways to decompose hierarchicalbehavior, such as into useful subprograms. In embodiments, ahierarchical RNN may be used to manage one or more hierarchicaltemplates for data collection in an industrial environment.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a stochasticneural network, which may introduce random variations into the network.Such random variations can be viewed as a form of statistical sampling,such as Monte Carlo sampling.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a genetic scalerecurrent neural network. In such embodiments, an RNN (often a LSTM) isused where a series is decomposed into a number of scales where everyscale informs the primary length between two consecutive points. A firstorder scale consists of a normal RNN, a second order consists of allpoints separated by two indices and so on. The Nth order RNN connectsthe first and last node. The outputs from all the various scales may betreated as a committee of members, and the associated scores may be usedgenetically for the next iteration.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a committee ofmachines (“CoM”), comprising a collection of different neural networksthat together “vote” on a given example. Because neural networks maysuffer from local minima, starting with the same architecture andtraining, but using randomly different initial weights often givesdifferent results. A CoM tends to stabilize the result.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an associativeneural network (“ASNN”), such as involving an extension of committee ofmachines that combines multiple feed forward neural networks and ak-nearest neighbor technique. It may use the correlation betweenensemble responses as a measure of distance amid the analyzed cases forthe kNN. This corrects the bias of the neural network ensemble. Anassociative neural network may have a memory that can coincide with atraining set. If new data become available, the network instantlyimproves its predictive ability and provides data approximation(self-learns) without retraining. Another important feature of ASNN isthe possibility to interpret neural network results by analysis ofcorrelations between data cases in the space of models.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an instantaneouslytrained neural network (“ITNN”), where the weights of the hidden and theoutput layers are mapped directly from training vector data.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a spiking neuralnetwork, which may explicitly consider the timing of inputs. The networkinput and output may be represented as a series of spikes (such as adelta function or more complex shapes). SNNs can process information inthe time domain (e.g., signals that vary over time, such as signalsinvolving dynamic behavior of industrial machines). They are oftenimplemented as recurrent networks.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a dynamic neuralnetwork that addresses nonlinear multivariate behavior and includeslearning of time-dependent behavior, such as transient phenomena anddelay effects. Transients may include behavior of shifting industrialcomponents, such as variable speeds of rotating shafts or other rotatingcomponents.

In embodiments, cascade correlation may be used as an architecture andsupervised learning algorithm, supplementing adjustment of the weightsin a network of fixed topology. Cascade-correlation may begin with aminimal network, then automatically trains and adds new hidden units oneby one, creating a multi-layer structure. Once a new hidden unit hasbeen added to the network, its input-side weights may be frozen. Thisunit then becomes a permanent feature-detector in the network, availablefor producing outputs or for creating other, more complex featuredetectors. The cascade-correlation architecture may learn quickly,determine its own size and topology, and retain the structures it hasbuilt even if the training set changes and requires no back-propagation.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a neuro-fuzzynetwork, such as involving a fuzzy inference system in the body of anartificial neural network. Depending on the type, several layers maysimulate the processes involved in a fuzzy inference, such asfuzzification, inference, aggregation and defuzzification. Embedding afuzzy system in a general structure of a neural net as the benefit ofusing available training methods to find the parameters of a fuzzysystem.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a compositionalpattern-producing network (“CPPN”), such as a variation of anassociative neural network (“ANN”) that differs the set of activationfunctions and how they are applied. While typical ANNs often containonly sigmoid functions (and sometimes Gaussian functions), CPPNs caninclude both types of functions and many others. Furthermore, CPPNs maybe applied across the entire space of possible inputs, so that they canrepresent a complete image. Since they are compositions of functions,CPPNs in effect encode images at infinite resolution and can be sampledfor a particular display at whatever resolution is optimal.

This type of network can add new patterns without re-training. Inembodiments, methods and systems described herein that involve an expertsystem or self-organization capability may use a one-shot associativememory network, such as by creating a specific memory structure, whichassigns each new pattern to an orthogonal plane using adjacentlyconnected hierarchical arrays.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchicaltemporal memory (“HTM”) neural network, such as involving the structuraland algorithmic properties of the neocortex. HTM may use a biomimeticmodel based on memory-prediction theory. HTM may be used to discover andinfer the high-level causes of observed input patterns and sequences.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a holographicassociative memory (“HAM”) neural network, which may comprise an analog,correlation-based, associative, stimulus-response system. Informationmay be mapped onto the phase orientation of complex numbers. The memoryis effective for associative memory tasks, generalization and patternrecognition with changeable attention.

In embodiments, various embodiments involving network coding may be usedto code transmission data among network nodes in neural net, such aswhere nodes are located in one or more data collectors or machines in anindustrial environment.

In embodiments, methods and systems include an expert system forprocessing a plurality of inputs collected from sensors in an industrialenvironment. An example system includes a modular neural network wherethe expert system uses one type of neural network for recognizing apattern and a different neural network for self-organizing an activityin the industrial environment. In embodiments, the pattern indicates afault condition of a machine. In embodiments, the self-organizedactivity governs autonomous control of a system in the environment. Inembodiments, the expert system organizes the activity based at least inpart on the recognized pattern.

In embodiments, methods and systems include an expert system forprocessing a plurality of inputs collected from sensors in an industrialenvironment. An example system includes a modular neural network, wherethe expert system uses one neural network for classifying an item and adifferent neural network for predicting a state of the item. Inembodiments, classifying an item includes at least one of identifying amachine, a component, and an operational mode of a machine in theenvironment. In embodiments, predicting a state includes predicting atleast one of a fault state, an operational state, an anticipated state,and a maintenance state.

In embodiments, methods and systems include an expert system forprocessing a plurality of inputs collected from sensors in an industrialenvironment. An example system includes a modular neural network, wherethe expert system uses one neural network for determining at least oneof a state and a context and a different neural network forself-organizing a process involving the at least one state or context.In embodiments, the stat or context includes at least one state of amachine, a process, a work flow, a marketplace, a storage system, anetwork, and a data collector. In embodiments, the self-organizedprocess includes at least one of a data storage process, a networkcoding process, a network selection process, a data marketplace process,a power generation process, a manufacturing process, a refining process,a digging process, and a boring process.

An expert system for processing a plurality of inputs collected fromsensors in an industrial environment, comprising: a modular neuralnetwork, comprising at least two neural networks selected from the groupconsisting of feed forward neural networks, radial basis function neuralnetworks, self-organizing neural networks, Kohonen self-organizingneural networks, recurrent neural networks, modular neural networks,artificial neural networks, physical neural networks, multi-layeredneural networks, convolutional neural networks, a hybrids of a neuralnetworks with another expert system, auto-encoder neural networks,probabilistic neural networks, time delay neural networks, convolutionalneural networks, regulatory feedback neural networks, radial basisfunction neural networks, recurrent neural networks, Hopfield neuralnetworks, Boltzmann machine neural networks, self-organizing map (“SOM”)neural networks, learning vector quantization (“LVQ”) neural networks,fully recurrent neural networks, simple recurrent neural networks, echostate neural networks, long short-term memory neural networks,bi-directional neural networks, hierarchical neural networks, stochasticneural networks, genetic scale RNN neural networks, committee ofmachines neural networks, associative neural networks, physical neuralnetworks, instantaneously trained neural networks, spiking neuralnetworks, neocognition neural networks, dynamic neural networks,cascading neural networks, neuro-fuzzy neural networks, compositionalpattern-producing neural networks, memory neural networks, hierarchicaltemporal memory neural networks, deep feed forward neural networks,gated recurrent unit (“GCU”) neural networks, auto encoder neuralnetworks, variational auto encoder neural networks, de-noising autoencoder neural networks, sparse auto-encoder neural networks, Markovchain neural networks, restricted Boltzmann machine neural networks,deep belief neural networks, deep convolutional neural networks,deconvolutional neural networks, deep convolutional inverse graphicsneural networks, generative adversarial neural networks, liquid statemachine neural networks, extreme learning machine neural networks, echostate neural networks, deep residual neural networks, support vectormachine neural networks, neural Turing machine neural networks, andholographic associative memory neural networks.

In embodiments, methods and systems include collecting data in anindustrial environment. An example system includes a physical neuralnetwork embodied in a mobile data collector. In embodiments, the mobiledata collector is adapted to be reconfigured by routing inputs invarying configurations, such that different neural net configurationsare enabled within the data collector for handling different types ofinputs. In embodiments, reconfiguration occurs under control of anexpert system. In embodiments, the expert system includes asoftware-based neural net. In embodiments, the software-based system islocated on the data collector. In embodiments, the software-based systemis located remotely from the data collector.

In embodiments, methods and systems include processing data collectedfrom an industrial environment. An example system includes a pluralityof neural networks deployed in a cloud platform that receives datastreams and other inputs collected from one or more industrialenvironments and transmitted to the cloud platform over one or morenetworks. In embodiments, the neural networks are of different types. Inembodiments, the plurality of neural networks includes at least onemodular neural network. In embodiments, the plurality of neural networksincludes at least one structure-adaptive neural network. In embodiments,the neural networks are structured to compete with each other undercontrol of an expert system, such as by processing input data sets fromthe same industrial environment to provide outputs and comparing theoutputs to at least one measure of success. In embodiments, a geneticalgorithm is used to facilitate variation and selection for thecompeting neural networks. In embodiments, the measure of successincludes at least one of the following measures: a measure of predictiveaccuracy, a measure of classification accuracy, an efficiency measure, aprofit measure, a maintenance measure, a safety measure, and a yieldmeasure.

In embodiments, an example system includes a network coding system forcoding transmission of data among network nodes in neural network. Inembodiments, the nodes comprise hardware devices located in at least oneof one or more data collectors, one or more storage systems, and one ormore network devices located in an industrial environment.

Within the data collection, monitoring, and control environment of theindustrial IoT are large and various sensor sets, which make efficientsetup and timely changes to sensor data collection a challenge.Continuous collection from all sensors may be impossible given the largenumber of sensors and limited resources, such as limited availability ofpower and limited data collection and management facilities, includingvarious limitations in availability and performance of sensor datacollection devices, input/output interfaces, data transfer facilities,data storage, data analysis facilities, and the like. The number ofsensors collected from at any given time must therefore be limited in anintelligent but timely manner, both at the time of setting up initialcollection and during the process of collection, including handlingrapid changes to a present collection scheme based on a change in stateof a system, operational conditions (e.g., an alert condition, change inoperational mode, etc.), or the like. Embodiments of the methods andsystems disclosed herein may therefore include rapid route creation andmodification for routing collectors, such as by taking advantage ofhierarchical templates, execution of smart route changes, monitoring andresponding to changes in operational conditions, and the like.

In embodiments, rapid route creation and modification for datacollection in an industrial environment may take advantage ofhierarchical templates. Templates may be used to take advantage of‘like’ machinery that can utilize the same hierarchical sensor routingscheme. For example, among many possible types of machines about whichdata may be collected, the members of a certain class of motor, such asa stepper motor class, may have very similar sensor routing needs, suchas for routine operations, routine maintenance, and failure modedetection, that may be described in a common hierarchy of sensorcollection routines. The user installing a new stepper motor may thenuse the ‘stepper motor hierarchical routing template’ for the new motor.After installation, the stepper motor hierarchical routing template maythen be used to change the routing schemes for changing conditions. Theuser may optionally make adjustments to the template as needed perunique motor functions, applications, environments, modes, and the like.The use of a template for deploying a routing scheme greatly reduces thetime a user requires to configure the routing scheme for a new motor, orto deploy new routing technologies on an existing system that utilizestraditional sensor collection methods. Once the hierarchical routingtemplate is in place, the sensor collection routine may be changedquickly based on the template, thus allowing for rapid routemodification under changing conditions, such as: a change in theoperating mode of the stepper motor that requires a different subset ofsensors for monitoring, a limit alert or failure indication thatrequires a more focused subset of sensors for use in diagnosing theproblem, and the like. Hierarchical routing templates thus allow forrapid deployment of sensor routing configurations, as well as allowingthe sensed industrial environment to be altered dynamically asconditions change.

A functional hierarchy of routing templates may include differenthierarchical configurations for a component, machine, system, industrialenvironment, and the like, including all sensors and a plurality ofconfigurations formed from a subset of all sensors. At a system level,an ‘all-sensor’ configuration may include: a connection map to allsensors in a system, mapping to all onboard instrumentation sensors(e.g., monitoring points reporting within a machine or set of machines),mapping to an environment's sensors (e.g., monitoring points around themachines/equipment, but not necessarily onboard), mapping to availablesensors on data collectors (e.g., data collectors that can be flexiblyprovisioned for particular data among different kinds), a unified mapcombining different individual mappings, and the like. A routingconfiguration may be provided, such as to indicate how to implement anoperational routing scheme, a scheduled maintenance routing scheme(e.g., collecting from a greater set of overall sensors than inoperational mode, but distributed across the system, or a focused sensorset for specific components, functions, and modes), one or more failuremode routing schemes for multiple focused sensor collection groupstargeting different failure mode analyses (e.g., for a motor, onefailure mode may be for bearings, another for startup speed-torque,where a different subset of sensor data is needed based on the failuremode, such as detected in anomalous readings taken during operations ormaintenance), power savings (e.g., weather conditions necessitatingreduced plant power), and the like.

As noted, hierarchical templates may also be conditional (e.g.,rule-based), such as templates with conditional routing based onparameters, such as sensed data during a first collection period, wherea subsequent routing configuration is varied. Within the hierarchy,nodes in a graph or tree may indicate forks by which conditional logicmay be used, such as to select a given subset of sensors for a givenoperational mode. Thus, the hierarchical template may be associated witha rule-based or model-based expert system, which may facilitateautomated routing based on the hierarchical template and based onobserved conditions, such as based on a type of machine and itsoperational state, environmental context, or the like. In a non-limitingexample, a hierarchical template may have an initial collectionconfiguration and a conditional hierarchy in place to switch from theinitial collection configuration to a second collection configurationbased on the sensed conditions of an initial sensor collection.Continuing this example, among various possible machines, a conveyorsystem may have a plurality of sensors for collection in an initialcollection, but once the first data is collected and analyzed, if theconveyor is determined to be in an idle state (such as due to theabsence of a signal above a minimum threshold on a motion sensor), thenthe system may switch to a sensor data collection regime that isappropriate for the idles state of the conveyor (e.g., using a verysmall subset of the plurality of sensors, such as just using the motionsensor to detect departure from the idle state, at which point theoriginal regime may be renewed and the rest of a sensor set may bere-engaged). Thus, when the collection of sensor data detects a changedcondition to a state, an operational mode, an environmental condition,or the like, the sensor data collection may be switched to anappropriate configuration.

Hierarchical templates for one collector may be based on coordination ofrouting with that of other collectors. For instance, a collector mightbe set up to perform vibration analysis while another collector is setup to perform pressure or temperature on each machine in a set ofsimilar machines, rather than having each machine collect all of thedata on each machine, where otherwise setup for different sensor typesmay be required for each collector for each machine. Factors such as theduration of sampling required, the time required to set up a givensensor, the amount of power consumed, the time available for collectionas a whole, the data rate of input/output of a sensor and/or thecollector, the bandwidth of a channel (wired or wireless) available fortransmission of collected data, and the like can be considered inarranging the coordination of the routing of two or more collectors,such that various parallel and serial configurations may be undertakento achieve an overall effectiveness. This may include optimizing thecoordination using an expert system, such as a rule-based optimization,a model-based optimization, or optimization using machine learning.

A machine learning system may create a hierarchical template structurefor improved routing, such as for teaching the system the defaultoperating conditions (e.g., normal operations mode, systems online andaverage production), peak operations mode (max capability), slackproduction, and the like. The machine learning system may create a newhierarchical template based on monitored conditions, such as a templatebased on a production level profile, a rate of production profile, adetected failure mode pattern analysis, and the like. The application ofa new machine learning created template may be based on a mode matchingbetween current production conditions and a machine learning templatecondition (e.g., the machine learning system creates a new template fora new production profile, and applies that new template whenever thatnew profile is detected).

Rapid route creation may be enabled using one or more hierarchicalrouting templates, such as when a routing template pre-establishes arouting scheme for different conditions, and when a trigger eventexecutes a change in the sensor routing scheme to accommodate thecondition. In embodiments, the trigger event may be an automatic changein routing based on a trigger that indicates a possible failure modethat forces a change in routing scheme from operational to failure modeanalysis; a human-executed change in routing scheme based on receivedsensor data; a learned routing change based on machine learning of whento trigger a change (e.g., as based on a machine being fed with a set ofhuman-executed or human-supervised changes); a manual routing change(e.g., optional to automatic/rapid automatic change); a human executedchange based on observed device performance; and the like. Routingchanges may include: changing from an operational mode to an acceleratedmaintenance, a failure mode analysis, a power saving mode ahigh-performance/high-output mode (e.g., for peak power in a generationplant), and the like.

Switching hierarchical template configurations may be executed based onconnectivity with end-device sensors. In a highly automated collectionrouting environment (e.g., an indoor networked assembly plant) differentrouting collection configurations may be employed for fixed and flexibleindustrial layouts. In a fixed industrial layout, such as a layout witha high degree of wired connectivity between end-device sensors,automated collectors, and networks, there may be different routingconfigurations for a network routing hierarchy portion, a collectorsensor-collection hierarchy portion, a storage portion, and the like.For a more flexible industrial layout with various wired and wirelessconnections between end-device sensors, automated collectors, andnetworks, there may be different schemes. For instance, a moderatelyautomated collection routing environment may include: automaticcollection and periodic network connection; a robot-carried collectorfor periodic collection (e.g., a ground-based robot, a drone, anunderwater device, a robot with network connection, a robot withintermittent network connection, a robot that periodically uploadscollection); a routing scheme with periodic collection and automatedrouting; a scheme only collecting periodically but routed directly uponcollection; a routing scheme with periodic collection and periodicautomated routing to collect periodically; and, over longer periods oftime, periodically routing multiple collections; and the like. For alower degree of automated collection routing, there may be a combinationof: automatic collection and human-aided collectors (e.g., humanscollecting alone, humans aided by robots), scheduled collection andhuman-aided collectors (e.g., humans initiating collection, humans aidedby robots for collection initiation, human launching a drone to collectdata at a remote site), and the like.

In embodiments, and referring to FIG. 137, hierarchical templates may beutilized by a local data collection system 10500 for collection andmonitoring of data collected through a plurality of input channels10500, such as data from sensors 10514, IoT devices 10516, and the like.The local collection system 10512, also referred to herein as a datacollector 10512, may comprise a data storage 10502; a data acquisitioncircuit 10504; a data analysis circuit 10506; and the like. Inembodiments, the monitoring facilities may be deployed: locally on thedata collector 10512; in part locally on the data collector and in parton a remote information technology infrastructure component apart fromthe data collector; and the like. A monitoring system may comprise aplurality of input channels communicatively coupled to the datacollector 10512. The data storage 10502 may be structured to store aplurality of collector route templates 10510 and sensor specificationsfor sensors 10514 that correspond to the input channels 10500. Inembodiments, the plurality of collector route templates 10510 eachcomprise a different sensor collection routine. The data acquisitioncircuit 10504 may be structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of the input channels 10500, and the data analysis circuit10506 structured to receive output data from the plurality of inputchannels 10500 and evaluate a current routing template collectionroutine based on the received output data. In embodiments, the datacollector 10512 is configured to switch from the current routingtemplate collection routine to an alternative routing templatecollection routine based on the content of the output data. Themonitoring system may further utilize a machine learning system (e.g., aneural network expert system), rule-based templates (e.g., based on anoperational state of a machine with respect to which the input channelsprovide information, the input channels provide information, the inputchannels provide information), smart route changes, alarm states,network connectivity, self-organization amongst a plurality of datacollectors, coordination of sensor groups, and the like.

In embodiments, evaluation of the current routing templates may be basedon operational mode routing collection schemes, such as a normaloperational mode, a peak operational mode, an idle operational mode, amaintenance operational mode, a power saving operational mode, and thelike. As a result of monitoring, the data collector may switch from acurrent routing template collection routine because the data analysiscircuit determines a change in operating modes, such as the operatingmode changing from an operational mode to an accelerated maintenancemode, the operating mode changing from an operational mode to a failuremode analysis mode, the operating mode changing from an operational modeto a power-saving mode, the operating mode changing from an operationalmode to a high-performance mode, and the like. The data collector mayswitch from a current routing template collection routine based on asensed change in a mode of operation, such as a failure condition, aperformance condition, a power condition, a temperature condition, avibration condition, and the like. The evaluation of the current routingtemplate collection routine may be based on a collection routine withrespect to a collection parameter, such as network availability, sensoravailability, a time-based collection routine (e.g., on a schedule, overtime), and the like.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collector communicatively coupled to aplurality of input channels; a data storage structured to store aplurality of collector route templates and sensor specifications forsensors that correspond to the input channels. In embodiments, theplurality of collector route templates each comprise a different sensorcollection routine; a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of the input channels; and a data analysiscircuit structured to receive output data from the plurality of inputchannels and evaluate a current routing template collection routinebased on the received output data. In embodiments, the data collector isconfigured to switch from the current routing template collectionroutine to an alternative routing template collection routine based onthe content of the output data. In embodiments, the system is deployedlocally on the data collector, in part locally on the data collector andin part on a remote information technology infrastructure componentapart from the collector, and the like. Each of the input channels maycorrespond to a sensor located in the environment. The evaluation of thecurrent routing template may be based on operational mode routingcollection schemes. The operational mode is at least one of a normaloperational mode, a peak operational mode, an idle operational mode, amaintenance operational mode, and a power saving operational mode. Thedata collector may switch from the current routing template collectionroutine because the data analysis circuit determines a change inoperating modes, such as where the operating mode changes from anoperational mode to an accelerated maintenance mode, from an operationalmode to a failure mode analysis mode, from an operational mode to apower saving mode, from an operational mode to high-performance mode,and the like. The data collector may switch from the current routingtemplate collection routine based on a sensed change in a mode ofoperation, such as where the sensed change is a failure condition, aperformance condition, a power condition, a temperature condition, avibration condition, and the like. The evaluation of the current routingtemplate collection routine may be based on a collection routine withrespect to a collection parameter, such as where the parameter isnetwork availability, sensor availability, a time-based collectionroutine (e.g., where a routine collects sensor data on a schedule,evaluates sensor data over time).

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data storage structured tostore a plurality of collector route templates and sensor specificationsfor sensors that correspond to the input channels. In embodiments, theplurality of collector route templates each comprise a different sensorcollection routine; providing a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channels;and providing a data analysis circuit structured to receive output datafrom the plurality of input channels and evaluate a current routingtemplate collection routine based on the received output data. Inembodiments, the data collector is configured to switch from the currentrouting template collection routine to an alternative routing templatecollection routine based on the content of the output data. Inembodiments, the computer-implemented method is deployed locally on thedata collector, such as deployed in part locally on the data collectorand in part on a remote information technology infrastructure componentapart from the collector, where each of the input channels correspond toa sensor located in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing adata collector communicatively coupled to a plurality of input channels;providing a data storage structured to store a plurality of collectorroute templates and sensor specifications for sensors that correspond tothe input channels. In embodiments, the plurality of collector routetemplates each comprise a different sensor collection routine; providinga data acquisition circuit structured to interpret a plurality ofdetection values, each of the plurality of detection valuescorresponding to at least one of the input channels; and providing adata analysis circuit structured to receive output data from theplurality of input channels and evaluate a current routing templatecollection routine based on the received output data. In embodiments,the data collector is configured to switch from the current routingtemplate collection routine to an alternative routing templatecollection routine based on the content of the output data. Inembodiments, the instructions may be deployed locally on the datacollector, such as deployed in part locally on the data collector and inpart on a remote information technology infrastructure component apartfrom the collector, where each of the input channels correspond to asensor located in the environment.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise a data collector communicatively coupled to aplurality of input channels; a data storage structured to store aplurality of collector route templates, sensor specifications forsensors that correspond to the input channels. In embodiments, theplurality of collector route templates each comprise a different sensorcollection routine; a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of the input channels; and a machinelearning data analysis circuit structured to receive output data fromthe plurality of input channels and evaluate a current routing templatecollection routine based on the received output data received over time.In embodiments, the machine learning data analysis circuit learnsreceived output data patterns. In embodiments, the data collector isconfigured to switch from the current routing template collectionroutine to an alternative routing template collection routine based onthe learned received output data patterns. In embodiments, themonitoring system may be deployed locally on the data collector, such asdeployed in part locally on the data collector and in part on a remoteinformation technology infrastructure component apart from thecollector, where each of the input channels correspond to a sensorlocated in the environment. The machine learning data analysis circuitmay include a neural network expert system. The evaluation of thecurrent routing template may be based on operational mode routingcollection schemes. The operational mode may be at least one of a normaloperational mode, a peak operational mode, an idle operational mode, amaintenance operational mode, and a power saving operational mode. Thedata collector may switch from the current routing template collectionroutine because the data analysis circuit determines a change inoperating modes, such as where the operating mode changes from anoperational mode to an accelerated maintenance mode, from an operationalmode to a failure mode analysis mode, from an operational mode to apower saving mode, from an operational mode to high-performance mode,and the like. The data collector may switch from the current routingtemplate collection routine based on a sensed change in a mode ofoperation, such as where the sensed change is a failure condition, aperformance condition, a power condition, a temperature condition, avibration condition, and the like. The evaluation of the current routingtemplate collection routine may be based on a collection routine withrespect to a collection parameter, such as where the parameter isnetwork availability, a sensor availability, a time-based collectionroutine (collects sensor data on a schedule, evaluates sensor data overtime).

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data storage structured tostore a plurality of collector route templates, sensor specificationsfor sensors that correspond to the input channels. In embodiments, theplurality of collector route templates each comprise a different sensorcollection routine; providing a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channels;and providing a machine learning data analysis circuit structured toreceive output data from the plurality of input channels and evaluate acurrent routing template collection routine based on the received outputdata received over time. In embodiments, the machine learning dataanalysis circuit learns received output data patterns. In embodiments,the data collector is configured to switch from the current routingtemplate collection routine to an alternative routing templatecollection routine based on the learned received output data patterns.In embodiments, the method may be deployed locally on the datacollector, such as deployed in part locally on the data collector and inpart on a remote information technology infrastructure component apartfrom the collector, where each of the input channels correspond to asensor located in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing adata collector communicatively coupled to a plurality of input channels;providing a data storage structured to store a plurality of collectorroute templates, sensor specifications for sensors that correspond tothe input channels. In embodiments, the plurality of collector routetemplates each comprise a different sensor collection routine; providinga data acquisition circuit structured to interpret a plurality ofdetection values, each of the plurality of detection valuescorresponding to at least one of the input channels; and providing amachine learning data analysis circuit structured to receive output datafrom the plurality of input channels and evaluate a current routingtemplate collection routine based on the received output data receivedover time. In embodiments, the machine learning data analysis circuitlearns received output data patterns. In embodiments, the data collectoris configured to switch from the current routing template collectionroutine to an alternative routing template collection routine based onthe learned received output data patterns. In embodiments, theinstructions may be deployed locally on the data collector, such asdeployed in part locally on the data collector and in part on a remoteinformation technology infrastructure component apart from thecollector, where each of the input channels correspond to a sensorlocated in the environment.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collector communicatively coupled to aplurality of input channels; a data storage structured to store acollector route template, sensor specifications for sensors thatcorrespond to the input channels. In embodiments, the collector routetemplate comprises a sensor collection routine; a data acquisitioncircuit structured to interpret a plurality of detection values, each ofthe plurality of detection values corresponding to at least one of theinput channels; and a data analysis circuit structured to receive outputdata from the plurality of input channels and evaluate the receivedoutput data with respect to a rule. In embodiments, the data collectoris configured to modify the sensor collection routine based on theapplication of the rule to the received output data. In embodiments, thesystem may be deployed locally on the data collector, such as deployedin part locally on the data collector and in part on a remoteinformation technology infrastructure component apart from thecollector, where each of the input channels correspond to a sensorlocated in the environment. The rule may be based on an operationalstate of a machine with respect to which the input channels provideinformation, on an anticipated state of a machine with respect to whichthe input channels provide information, on a detected fault condition ofa machine with respect to which the input channels provide information,and the like. The evaluation of the received output data may be based onoperational mode routing collection schemes, where the operational modeis at least one of a normal operational mode, a peak operational mode,an idle operational mode, a maintenance operational mode, and a powersaving operational mode. The data collector may modify the sensorcollection routine because the data analysis circuit determines a changein operating modes, such as where the operating mode changes from anoperational mode to an accelerated maintenance mode, from an operationalmode to a failure mode analysis mode, from an operational mode to apower saving mode, from an operational mode to high-performance mode,and the like. The data collector may modify the sensor collectionroutine based on a sensed change in a mode of operation, such as wherethe sensed change is a failure condition, a performance condition, apower condition, a temperature condition, a vibration condition, and thelike. The evaluation of the received output data may be based on acollection routine with respect to a collection parameter. Inembodiments, the parameter is a network availability, a sensoravailability, a time-based collection routine (e.g., collects sensordata on a schedule or over time), and the like.

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data storage structured tostore a collector route template, sensor specifications for sensors thatcorrespond to the input channels. In embodiments, the collector routetemplate comprises a sensor collection routine; providing a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of the input channels; and providing a data analysis circuitstructured to receive output data from the plurality of input channelsand evaluate the received output data with respect to a rule. Inembodiments, the data collector is configured to modify the sensorcollection routine based on the application of the rule to the receivedoutput data. In embodiments, the method may be deployed locally on thedata collector, such as deployed in part locally on the data collectorand in part on a remote information technology infrastructure componentapart from the collector, where each of the input channels correspond toa sensor located in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing adata collector communicatively coupled to a plurality of input channels;providing a data storage structured to store a collector route template,sensor specifications for sensors that correspond to the input channels.In embodiments, the collector route template comprises a sensorcollection routine; providing a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channels;and providing a data analysis circuit structured to receive output datafrom the plurality of input channels and evaluate the received outputdata with respect to a rule. In embodiments, the data collector isconfigured to modify the sensor collection routine based on theapplication of the rule to the received output data. In embodiments, theinstructions may be deployed locally on the data collector, such asdeployed in part locally on the data collector and in part on a remoteinformation technology infrastructure component apart from thecollector, where each of the input channels correspond to a sensorlocated in the environment.

Rapid route creation and modification in an industrial environment mayemploy smart route changes based on incoming data or alarms, such aschanges enabling dynamic selection of data collection for analysis orcorrelation. Smart route changes may enable the system to alter currentrouting of sensor data based on incoming data or alarms. For instance, auser may set up a routing configuration that establishes a schedule ofsensor collection for analysis, but when the analysis (or an alarm)indicates a special need, the system may change the sensor routing toaddress that need. For example, in the case where a change in a motorvibration profile (as one example among any of the machines describedthroughout this disclosure), such as rapidly increasing the peakamplitude of shaking on at least one axis of a vibration sensor set,that indicates a potential early failure of the motor, the system maychange the routing to collect more focused data collection for analysis,such as initiating collection on more axes of the motor, initiatingcollection on additional bearings of the motor, and/or initiatingcollection using other sensors (such as temperature or heat fluxsensors), that may confirm an initial hypothesis that the failure modeis occurring or otherwise assist in analysis of the state or operationalcondition of the machine.

Detected operational mode changes may trigger a rapid route change. Forinstance, an operational mode may be detected as the result of asingle-point sensor out-of-range detection, an analysis determination,and the like, and generate a routing change. An analysis determinationmay be detected from a sensor end-point, such as through a single-pointsensor analysis, a multiple-point sensor analysis, an analysis domainanalysis (e.g., through a time profile, frequency profile, correlatedmulti-point determination), and the like. In another instance, amaintenance mode may be detected during routine maintenance, where arouting change increases data collection to capture data at a higherrate under an anomalous condition. A failure mode may be detected, suchas through an alarm that indicates near-term potential for a failure ofa machine that triggers increased data capture rate for analysis.Performance-based modes may be detected, such as detecting a level ofoutput rate (e.g., peak, slack, idle), which may then initiate changesin routing to accommodate the analysis needs for the differentperformance monitoring and metrics associated with the state. Forexample, if a high peak speed is detected for a motor, a conveyor, anassembly line, a generator, a turbine, or the like, relative tohistorical measurements over some time period, additional sensors may beengaged to watch for failures that are typically associated with peakspeeds, such as overheating (as measured by engaging a temperature orheat flux sensor), excessive noise (as measured by an acoustic or noisesensor), excessive shaking (as measured by one or more vibrationsensors), or the like.

Alarm detections may trigger a rapid route change. Alarm sources mayinclude a front-end collector, local intelligence resource, back-enddata analysis process, ambient environment detector, network qualitydetector, power quality detector, heat, smoke, noise, flooding, and thelike. Alarm types may include a single-instance anomaly detection,multiple-instance anomaly detection, simultaneous multi-sensordetection, time-clustered sensor detection (e.g., a single sensor ormultiple sensors), frequency-profile detection (e.g., increasing rate ofanomaly detection such as an alarm increasing in its occurrence overtime, a change in a frequency component of a sensor output such as amotor's physical vibration profile changing over time), and the like.

A machine learning system may change routing based on learned alarmpattern analysis. The machine learning system may learn system alarmcondition patterns, such as alarm conditions expected under normaloperating conditions, under peak operating conditions, expected overtime based on age of components (e.g., new, during operational life,during extended life, during a warrantee period), and the like. Themachine learning system may change routing based on a change in an alarmpattern, such as a system operating normally but experiencing a peakoperating alarm pattern (e.g., a system running when it should not be),a system is new but experiencing an older profile (e.g., detection ofinfant mortality), and the like. The machine learning system may changerouting based on a current alarm profile vs. an expected change inproduction condition. For example, a plant, system, or component isexperiencing above average alarm conditions just before a ramp-up ofproduction (e.g., could be foretelling of above average failures duringincreased production), just before going slack (e.g., could be anopportunity to ramp up maintenance procedures based on increased datataking routing scheme), after an unplanned event (e.g., weather, poweroutage, restart), and the like.

A rapid route change action may include: an increased rate of sampling(e.g., to a single sensor, to multiple sensors), an increase in thenumber of sensors being sampled (e.g., simultaneous sampling of othersensors on a device, coordinated sampling of similar sensors on near-bydevices), generating a burst of sampling (e.g., sampling at a high ratefor a period of time), and the like. Actions may be executed on aschedule, coordinated with a trigger, based on an operational mode, andthe like. Triggered actions may include: anomalous data, an exceededthreshold level, an operational event trigger (e.g., at startupcondition such as for startup motor torque), and the like.

A rapid route change may switch between routing schemes, such as anoperational routing scheme (e.g., a subset of sensor collection fornormal operations), a scheduled maintenance routing scheme (e.g., anincreased and focused set of sensor collection than for normaloperations), and the like. The distribution of sensor data may bechanged, such as to distribute sensor collection across the system, suchas for a sensor collection set for specific components, functions, andmodes. A failure mode routing scheme may entail multiple focused sensorcollection groups targeting different failure mode analyses (e.g., for amotor, one failure mode may be for bearings, another for startupspeed-torque) where a different subset of sensor data may be neededbased on the failure mode (e.g., as detected in anomalous readings takenduring operations or maintenance). Power saving mode routing may beexecuted when weather conditions necessitate reduced plant power.

Dynamic adjustment of route changes may be executed based onconnectivity factors, such as the factors associated with the collectoror network availability and bandwidth. For example, routing may bechanged for a device associated with an alarm detection, where changingrouting for targeted devices on the network frees up bandwidth. Changesto routing may have a duration, such as only for a pre-determined periodof time and then switching back, maintaining a change untiluser-directed, changing duration based on network availability, and thelike.

In embodiments, and referring to FIG. 139, smart route changes may beimplemented by a local data collection system 10520 for collection andmonitoring of data collected through a plurality of input channels10500, such as data from sensors 10522, IoT devices 10524, and the like.The local data collection system 102, also referred to herein as a datacollector 10520, may comprise a data storage 10502, a data acquisitioncircuit 10504, a data analysis circuit 10506, a response circuit 10508,and the like. In embodiments, the monitoring facilities may be deployedlocally on the data collector 10520, in part locally on the datacollector and in part on a remote information technology infrastructurecomponent apart from the data collector, and the like. Smart routechanges may be implemented between data collectors, such as where astate message is transmitted between the data collectors (e.g., from aninput channel that is mounted in proximity to a second input channel,from a related group of input sensors, and the like). A monitoringsystem may comprise a plurality of input channels 10500 communicativelycoupled to the data collector 10520. The data acquisition circuit 10504may be structured to interpret a plurality of detection values, each ofthe plurality of detection values corresponding to at least one of theinput channels 10500. In embodiments, the data acquisition circuit 10504acquires sensor data from a first route of input channels for theplurality of input channels. The data storage 10502 may be structured tostore sensor data, sensor specifications, and the like, for sensors10522 that correspond to the input channels 10500. The data analysiscircuit 10506 may be structured to evaluate the sensor data with respectto stored anticipated state information. In embodiments, the anticipatedstate information may include an alarm threshold level, and. Inembodiments, the data analysis circuit 10506 sets an alarm state whenthe alarm threshold level is exceeded for a first input channel in thefirst group of input channels. Further, the data analysis circuit 10506may transmit the alarm state across a network to a routing controlfacility having the response circuit 10508. The response circuit 10508may be structured to change the routing of the input channels for datacollection from the first routing of input channels to an alternaterouting of input channels upon reception of a routing change indicationfrom the routing control facility. In the case of a networktransmission, the alternate routing of input channels may include thefirst input channel and a group of input channels related to the firstinput channel, where the data collector executes the change in routingof the input channels if a communication parameter of the networkbetween the data collector and the routing control facility is not met(e.g., a time-period parameter, a network connection and/or bandwidthavailability parameter).

In embodiments, an alarm state may indicate a detection mode, such as anoperational mode detection comprising an out-of-range detection, amaintenance mode detection comprising an alarm detected duringmaintenance, a failure mode detection (e.g., where the controllercommunicates a failure mode detection facility), a power mode detection.In embodiments, the alarm state is indicative of a power relatedlimitation data of the anticipated state information, a performance modedetection. In embodiments, the alarm state is indicative of ahigh-performance limitation data of the anticipated state information,and the like. The monitoring system may further include the analysiscircuit setting the alarm state when the alarm threshold level isexceeded for an alternate input channel in the first group of inputchannels, such as where the setting of the alarm state for the firstinput channel and the alternate input channel are determined to be amultiple-instance anomaly detection. In embodiments, the second routingof input channels comprises the first input channel and a second inputchannel. In embodiments, the sensor data from the first input channeland the second input channel contribute to simultaneous data analysis.The second routing of input channels may include a change in a routingcollection parameter, such as where the routing collection parameter isan increase in sampling rate, an increase in the number of channelsbeing sampled, a burst sampling of at least one of the plurality ofinput channels, and the like.

In embodiments, and referring to FIG. 138, the collector route templates10510 may be utilized for smart route changes and may be implemented bya local data collection system 10512 for collection and monitoring ofdata collected through a plurality of input channels 10500, such as datafrom sensors 10514, IoT devices 10516, and the like. The localcollection system 10512, also referred to herein as a data collector10512, may comprise a data storage 10502, a data acquisition circuit10504, a data analysis circuit 10506, a response circuit 10508, and thelike. In embodiments, the monitoring facilities may be deployed locallyon the data collector 10512, in part locally on the data collector andin part on a remote information technology infrastructure componentapart from the data collector, and the like.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collector communicatively coupled to aplurality of input channels; a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channels. Inembodiments, the data acquisition circuit acquires sensor data from afirst route of input channels for the plurality of input channels; adata storage structured to store sensor specifications for sensors thatcorrespond to the input channels; a data analysis circuit structured toevaluate the sensor data with respect to stored anticipated stateinformation. In embodiments, the anticipated state information comprisesan alarm threshold level. In embodiments, the data analysis circuit setsan alarm state when the alarm threshold level is exceeded for a firstinput channel in the first group of input channels; and a responsecircuit structured to change the routing of the input channels for datacollection from the first routing of input channels to an alternaterouting of input channels. In embodiments, the alternate routing ofinput channels comprise the first input channel and a group of inputchannels related to the first input channel. In embodiments, the systemmay be deployed locally on the data collector, deployed in part locallyon the data collector and in part on a remote information technologyinfrastructure component apart from the collector. In embodiments, eachof the input channels correspond to a sensor located in the environment.The group of input channels may be related to the first input channelare at least in part taken from the plurality of input channels notincluded in the first routing of input channels. An alarm state mayindicate a detection mode, such as where the detection mode is anoperational mode detection comprising an out-of-range detection, thedetection mode is a maintenance mode detection comprising an alarmdetected during maintenance, the detection mode is a failure modedetection. The controller may communicate the failure mode detectionfacility, such as where the detection mode is a power mode detection andthe alarm state are indicative of a power related limitation data of theanticipated state information, the detection mode is a performance modedetection and the alarm state are indicative of a high-performancelimitation data of the anticipated state information, and the like. Theanalysis circuit may set the alarm state when the alarm threshold levelis exceeded for an alternate input channel in the first group of inputchannels, such as where the setting of the alarm state for the firstinput channel and the alternate input channel are determined to be amultiple-instance anomaly detection. In embodiments, the alternaterouting of input channels comprises the first input channel and a secondinput channel. In embodiments, the sensor data from the first inputchannel and the second input channel contribute to simultaneous dataanalysis. The alternate routing of input channels may include a changein a routing collection parameter, such as for an increase in samplingrate, an increase in the number of channels being sampled, a burstsampling of at least one of the plurality of input channels, and thelike.

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of the inputchannels. In embodiments, the data acquisition circuit acquires sensordata from a first route of input channels for the plurality of inputchannels; providing a data storage structured to store sensorspecifications for sensors that correspond to the input channels;providing a data analysis circuit structured to evaluate the sensor datawith respect to stored anticipated state information. In embodiments,the anticipated state information comprises an alarm threshold level. Inembodiments, the data analysis circuit sets an alarm state when thealarm threshold level is exceeded for a first input channel in the firstgroup of input channels; and providing a response circuit structured tochange the routing of the input channels for data collection from thefirst routing of input channels to an alternate routing of inputchannels. In embodiments, the alternate routing of input channelscomprise the first input channel and a group of input channels relatedto the first input channel. In embodiments, the system may be deployedlocally on the data collector, deployed in part locally on the datacollector and in part on a remote information technology infrastructurecomponent apart from the collector. In embodiments, each of the inputchannels correspond to a sensor located in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions may comprise: providinga data collector communicatively coupled to a plurality of inputchannels; providing a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of the input channels. In embodiments, thedata acquisition circuit acquires sensor data from a first route ofinput channels for the plurality of input channels; providing a datastorage structured to store sensor specifications for sensors thatcorrespond to the input channels; providing a data analysis circuitstructured to evaluate the sensor data with respect to storedanticipated state information. In embodiments, the anticipated stateinformation comprises an alarm threshold level. In embodiments, the dataanalysis circuit sets an alarm state when the alarm threshold level isexceeded for a first input channel in the first group of input channels;and providing a response circuit structured to change the routing of theinput channels for data collection from the first routing of inputchannels to an alternate routing of input channels. In embodiments, thealternate routing of input channels comprise the first input channel anda group of input channels related to the first input channel. Inembodiments, the instructions may be deployed locally on the datacollector, deployed in part locally on the data collector and in part ona remote information technology infrastructure component apart from thecollector. In embodiments, each of the input channels correspond to asensor located in the environment.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collector communicatively coupled to aplurality of input channels; a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channels. Inembodiments, the data acquisition circuit acquires sensor data from afirst route of input channels for the plurality of input channels; adata storage structured to store sensor specifications for sensors thatcorrespond to the input channels; a data analysis circuit structured toevaluate the sensor data with respect to stored anticipated stateinformation. In embodiments, the anticipated state information comprisesan alarm threshold level. In embodiments, the data analysis circuit setsan alarm state when the alarm threshold level is exceeded for a firstinput channel in the first group of input channels and transmits thealarm state across a network to a routing control facility; and aresponse circuit structured to change the routing of the input channelsfor data collection from the first routing of input channels to analternate routing of input channels upon reception of a routing changeindication from the routing control facility. In embodiments, thealternate routing of input channels comprise the first input channel anda group of input channels related to the first input channel. Inembodiments, the data collector automatically executes the change inrouting of the input channels if a communication parameter of thenetwork between the data collector and the routing control facility isnot met. In embodiments, the instructions may be deployed locally on thedata collector, deployed in part locally on the data collector and inpart on a remote information technology infrastructure component apartfrom the collector. In embodiments, each of the input channelscorrespond to a sensor located in the environment. The communicationparameter may be a time-period parameter within which the routingcontrol facility must respond. The communication parameter may be anetwork availability parameter, such as a network connection parameteror bandwidth requirement. The group of input channels related to thefirst input channel may be at least in part taken from the plurality ofinput channels not included in the first routing of input channels. Thealarm state may indicate a detection mode, such as an operational modedetection comprising an out-of-range detection, a maintenance modedetection comprising an alarm detected during maintenance, and the like.The detection mode may be a failure mode detection, such as when thecontroller communicates the failure mode detection facility, the alarmstate is indicative of a power related limitation data of theanticipated state information, the detection mode is a performance modedetection where the alarm state is indicative of a high-performancelimitation data of the anticipated state information, and the like. Theanalysis circuit may set the alarm state when the alarm threshold levelis exceeded for an alternate input channel in the first group of inputchannels, such as where the setting of the alarm state for the firstinput channel and the alternate input channel are determined to be amultiple-instance anomaly detection. In embodiments, the alternaterouting of input channels comprises the first input channel and a secondinput channel. In embodiments, the sensor data from the first inputchannel and the second input channel contribute to simultaneous dataanalysis. The alternate routing of input channels may be a change in arouting collection parameter, such as an increase in sampling rate, isan increase in the number of channels being sampled, a burst sampling ofat least one of the plurality of input channels, and the like.

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of the inputchannels. In embodiments, the data acquisition circuit acquires sensordata from a first route of input channels for the plurality of inputchannels; providing a data storage structured to store sensorspecifications for sensors that correspond to the input channels;providing a data analysis circuit structured to evaluate the sensor datawith respect to stored anticipated state information. In embodiments,the anticipated state information comprises an alarm threshold level. Inembodiments, the data analysis circuit sets an alarm state when thealarm threshold level is exceeded for a first input channel in the firstgroup of input channels and transmits the alarm state across a networkto a routing control facility; and providing a response circuitstructured to change the routing of the input channels for datacollection from the first routing of input channels to an alternaterouting of input channels upon reception of a routing change indicationfrom the routing control facility. In embodiments, the alternate routingof input channels comprise the first input channel and a group of inputchannels related to the first input channel. In embodiments, the datacollector automatically executes the change in routing of the inputchannels if a communication parameter of the network between the datacollector and the routing control facility is not met. In embodiments,the instructions may be deployed locally on the data collector, deployedin part locally on the data collector and in part on a remoteinformation technology infrastructure component apart from thecollector. In embodiments, each of the input channels correspond to asensor located in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing adata collector communicatively coupled to a plurality of input channels;providing a data acquisition circuit structured to interpret a pluralityof detection values, each of the plurality of detection valuescorresponding to at least one of the input channels. In embodiments, thedata acquisition circuit acquires sensor data from a first route ofinput channels for the plurality of input channels; providing a datastorage structured to store sensor specifications for sensors thatcorrespond to the input channels; providing a data analysis circuitstructured to evaluate the sensor data with respect to storedanticipated state information. In embodiments, the anticipated stateinformation comprises an alarm threshold level. In embodiments, the dataanalysis circuit sets an alarm state when the alarm threshold level isexceeded for a first input channel in the first group of input channelsand transmits the alarm state across a network to a routing controlfacility; and providing a response circuit structured to change therouting of the input channels for data collection from the first routingof input channels to an alternate routing of input channels uponreception of a routing change indication from the routing controlfacility. In embodiments, the alternate routing of input channelscomprise the first input channel and a group of input channels relatedto the first input channel. In embodiments, the data collectorautomatically executes the change in routing of the input channels if acommunication parameter of the network between the data collector andthe routing control facility is not met. In embodiments, theinstructions may be deployed locally on the data collector, deployed inpart locally on the data collector and in part on a remote informationtechnology infrastructure component apart from the collector. Inembodiments, each of the input channels correspond to a sensor locatedin the environment.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a first and second data collectorcommunicatively coupled to a plurality of input channels; a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of the input channels. In embodiments, the data acquisitioncircuit acquires sensor data from a first route of input channels forthe plurality of input channels; a data storage structured to storesensor specifications for sensors that correspond to the input channels;a data analysis circuit structured to evaluate the sensor data withrespect to stored anticipated state information. In embodiments, theanticipated state information comprises an alarm threshold level. Inembodiments, the data analysis circuit sets an alarm state when thealarm threshold level is exceeded for a first input channel in the firstgroup of input channels; a communication circuit structured tocommunicate with a second data collector. In embodiments, the seconddata collector transmits a state message related to a first inputchannel from the first route of input channels; and a response circuitstructured to change the routing of the input channels for datacollection from the first routing of input channels to an alternaterouting of input channels based on the state message from the seconddata collector. In embodiments, the alternate routing of input channelcomprise the first input channel and a group of input channels relatedto the first input sensor. In embodiments, the system may be deployedlocally on the data collector, deployed in part locally on the datacollector and in part on a remote information technology infrastructurecomponent apart from the collector. In embodiments, each of the inputchannels correspond to a sensor located in the environment. The setstate message transmitted from the second data collector may be from asecond input channel that is mounted in proximity to the first inputchannel. The set alarm transmitted from the second controller may befrom a second input sensor that is part of a related group of inputsensors comprising the first input sensor. The group of input channelsrelated to the first input channel may be at least in part taken fromthe plurality of input channels not included in the first routing ofinput channels. The alarm state may indicate a detection mode, such aswhere the detection mode is an operational mode detection comprising anout-of-range detection, a maintenance mode detection comprising an alarmdetected during maintenance, is a failure mode detection, and the like.The controller may communicate the failure mode detection facility, suchas where the detection mode is a power mode detection and the alarmstate are indicative of a power related limitation data of theanticipated state information, the detection mode is a performance modedetection where the alarm state is indicative of a high-performancelimitation data of the anticipated state information, and the like. Theanalysis circuit may set the alarm state when the alarm threshold levelis exceeded for an alternate input channel in the first group of inputchannels, such as where the setting of the alarm state for the firstinput channel and the alternate input channel are determined to be amultiple-instance anomaly detection. In embodiments, the alternaterouting of input channels comprises the first input channel and a secondinput channel. In embodiments, the sensor data from the first inputchannel and the second input channel contribute to simultaneous dataanalysis. The alternate routing of input channels may be a change in arouting collection parameter, such as an increase in sampling rate, anincrease in the number of channels being sampled, a burst sampling of atleast one of the plurality of input channels, and the like.

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a first and second data collector communicativelycoupled to a plurality of input channels; providing a data acquisitioncircuit structured to interpret a plurality of detection values, each ofthe plurality of detection values corresponding to at least one of theinput channels. In embodiments, the data acquisition circuit acquiressensor data from a first route of input channels for the plurality ofinput channels; providing a data storage structured to store sensorspecifications for sensors that correspond to the input channels;providing a data analysis circuit structured to evaluate the sensor datawith respect to stored anticipated state information. In embodiments,the anticipated state information comprises an alarm threshold level. Inembodiments, the data analysis circuit sets an alarm state when thealarm threshold level is exceeded for a first input channel in the firstgroup of input channels; providing a communication circuit structured tocommunicate with a second data collector. In embodiments, the seconddata collector transmits a state message related to a first inputchannel from the first route of input channels, and providing a responsecircuit structured to change the routing of the input channels for datacollection from the first routing of input channels to an alternaterouting of input channels based on the state message from the seconddata collector. In embodiments, the alternate routing of input channelcomprise the first input channel and a group of input channels relatedto the first input sensor. In embodiments, the method may be deployedlocally on the data collector, deployed in part locally on the datacollector and in part on a remote information technology infrastructurecomponent apart from the collector. In embodiments, each of the inputchannels correspond to a sensor located in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing afirst and second data collector communicatively coupled to a pluralityof input channels; providing a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channels. Inembodiments, the data acquisition circuit acquires sensor data from afirst route of input channels for the plurality of input channels;providing a data storage structured to store sensor specifications forsensors that correspond to the input channels; providing a data analysiscircuit structured to evaluate the sensor data with respect to storedanticipated state information. In embodiments, the anticipated stateinformation comprises an alarm threshold level. In embodiments, the dataanalysis circuit sets an alarm state when the alarm threshold level isexceeded for a first input channel in the first group of input channels;providing a communication circuit structured to communicate with asecond data collector. In embodiments, the second data collectortransmits a state message related to a first input channel from thefirst route of input channels, and providing a response circuitstructured to change the routing of the input channels for datacollection from the first routing of input channels to an alternaterouting of input channels based on the state message from the seconddata collector. In embodiments, the alternate routing of input channelcomprise the first input channel and a group of input channels relatedto the first input sensor. In embodiments, the instructions may bedeployed locally on the data collector, deployed in part locally on thedata collector and in part on a remote information technologyinfrastructure component apart from the collector. In embodiments, eachof the input channels correspond to a sensor located in the environment.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collector communicatively coupled to aplurality of input channels; a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channel. Inembodiments, the data acquisition circuit acquires sensor data from afirst group of input channels from the plurality of input channels; adata storage structured to store sensor specifications for sensors thatcorrespond to the input channels; a data analysis circuit structured toevaluate the sensor data with respect to stored anticipated stateinformation. In embodiments, the anticipated state information comprisesan alarm threshold level. In embodiments, the data analysis circuit setsan alarm state when the alarm threshold level is exceeded for a firstinput channel in the first group of input channel; and a responsecircuit structured to change the input channels being collected from thefirst group of input channels to an alternative group of input channels.In embodiments, the alternate group of input channels comprise the firstinput channel and a group of input channels related to the first inputsensor. In embodiments, the system may be deployed locally on the datacollector, deployed in part locally on the data collector and in part ona remote information technology infrastructure component apart from thecollector. In embodiments, each of the input channels correspond to asensor located in the environment. The group of input sensors related tothe first input sensor may be at least in part taken from the pluralityof input sensors not included in the first group of input sensors. Thefirst group of input channels related to the first input channel may beat least in part taken from the plurality of input channels not includedin the first routing of input channels. The alarm state may indicate adetection mode, such as where the detection mode is an operational modedetection comprising an out-of-range detection, a maintenance modedetection comprising an alarm detected during maintenance. The detectionmode may be a failure mode detection, such as where the controllercommunicates the failure mode detection facility. The detection mode maybe a power mode detection where the alarm state is indicative of a powerrelated limitation data of the anticipated state information. Thedetection mode may be a performance mode detection, where the alarmstate is indicative of a high-performance limitation data of theanticipated state information. The analysis circuit may set the alarmstate when the alarm threshold level is exceeded for an alternate inputchannel in the first group of input channels, such as when the settingof the alarm state for the first input channel and the alternate inputchannel are determined to be a multiple-instance anomaly detection. Inembodiments, the alternate routing of input channels comprises the firstinput channel and a second input channel. In embodiments, the sensordata from the first input channel and the second input channelcontribute to simultaneous data analysis. An alternative group of inputchannels may include a change in a routing collection parameter, such aswhere the routing collection parameter is an increase in sampling rate,an increase in the number of channels being sampled, a burst sampling ofat least one of the plurality of input channels, and the like.

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of the inputchannel. In embodiments, the data acquisition circuit acquires sensordata from a first group of input channels from the plurality of inputchannels; providing a data storage structured to store sensorspecifications for sensors that correspond to the input channels;providing a data analysis circuit structured to evaluate the sensor datawith respect to stored anticipated state information. In embodiments,the anticipated state information comprises an alarm threshold level. Inembodiments, the data analysis circuit sets an alarm state when thealarm threshold level is exceeded for a first input channel in the firstgroup of input channel; and providing a response circuit structured tochange the input channels being collected from the first group of inputchannels to an alternative group of input channels. In embodiments, thealternate group of input channels comprise the first input channel and agroup of input channels related to the first input sensor. Inembodiments, the method may be deployed locally on the data collector,deployed in part locally on the data collector and in part on a remoteinformation technology infrastructure component apart from thecollector. In embodiments, each of the input channels correspond to asensor located in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing adata collector communicatively coupled to a plurality of input channels;providing a data acquisition circuit structured to interpret a pluralityof detection values, each of the plurality of detection valuescorresponding to at least one of the input channel. In embodiments, thedata acquisition circuit acquires sensor data from a first group ofinput channels from the plurality of input channels; providing a datastorage structured to store sensor specifications for sensors thatcorrespond to the input channels; providing a data analysis circuitstructured to evaluate the sensor data with respect to storedanticipated state information. In embodiments, the anticipated stateinformation comprises an alarm threshold level. In embodiments, the dataanalysis circuit sets an alarm state when the alarm threshold level isexceeded for a first input channel in the first group of input channel;and providing a response circuit structured to change the input channelsbeing collected from the first group of input channels to an alternativegroup of input channels. In embodiments, the alternate group of inputchannels comprise the first input channel and a group of input channelsrelated to the first input sensor. In embodiments, the instructions maybe deployed locally on the data collector, deployed in part locally onthe data collector and in part on a remote information technologyinfrastructure component apart from the collector. In embodiments, eachof the input channels correspond to a sensor located in the environment.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collector communicatively coupled to aplurality of input channels; a data storage structured to store aplurality of collector route templates, sensor specifications forsensors that correspond to the input channels. In embodiments, theplurality of collector route templates each comprise a different sensorcollection routine; a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of the input channels. In embodiments, thedata acquisition circuit acquires sensor data from a first route ofinput channels; and a data analysis circuit structured to evaluate thesensor data with respect to stored anticipated state information. Inembodiments, the anticipated state information comprises an alarmthreshold level. In embodiments, the data analysis circuit sets an alarmstate when the alarm threshold level is exceeded for a first inputchannel in the first group of input channels. In embodiments, the datacollector is configured to switch from a current routing templatecollection routine to an alternate routing template collection routinebased on a setting of an alarm state. In embodiments, the system may bedeployed locally on the data collector, deployed in part locally on thedata collector and in part on a remote information technologyinfrastructure component apart from the collector. In embodiments, eachof the input channels correspond to a sensor located in the environment.The setting of the alarm state may be based on operational mode routingcollection schemes, such as where the operational mode is at least oneof a normal operational mode, a peak operational mode, an idleoperational mode, a maintenance operational mode, and a power savingoperational mode. The alarm threshold level may be associated with asensed change to one of the plurality of input channels, such as wherethe sensed change is a failure condition, is a performance condition, apower condition, a temperature condition, a vibration condition, and thelike. The alarm state may indicate a detection mode, such as where thedetection mode is an operational mode detection comprising anout-of-range detection, a maintenance mode detection comprising an alarmdetected during maintenance, and the like. The detection mode may be apower mode detection, where the alarm state is indicative of a powerrelated limitation data of the anticipated state information. Thedetection mode may be a performance mode detection, where the alarmstate is indicative of a high-performance limitation data of theanticipated state information. The analysis circuit may set the alarmstate when the alarm threshold level is exceeded for an alternate inputchannel, such as where the setting of the alarm state is determined tobe a multiple-instance anomaly detection. The alternate routing templatemay be a change to an input channel routing collection parameter. Therouting collection parameter may be an increase in sampling rate, suchas an increase in the number of channels being sampled, a burst samplingof at least one of the plurality of input channels, and the like.

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data storage structured tostore a plurality of collector route templates, sensor specificationsfor sensors that correspond to the input channels. In embodiments, theplurality of collector route templates each comprise a different sensorcollection routine; providing a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channels. Inembodiments, the data acquisition circuit acquires sensor data from afirst route of input channels; and providing a data analysis circuitstructured to evaluate the sensor data with respect to storedanticipated state information. In embodiments, the anticipated stateinformation comprises an alarm threshold level. In embodiments, the dataanalysis circuit sets an alarm state when the alarm threshold level isexceeded for a first input channel in the first group of input channels.In embodiments, the data collector is configured to switch from acurrent routing template collection routine to an alternate routingtemplate collection routine based on a setting of an alarm state. Inembodiments, the system may be deployed locally on the data collector,deployed in part locally on the data collector and in part on a remoteinformation technology infrastructure component apart from thecollector. In embodiments, each of the input channels correspond to asensor located in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing adata collector communicatively coupled to a plurality of input channels;providing a data storage structured to store a plurality of collectorroute templates, sensor specifications for sensors that correspond tothe input channels. In embodiments, the plurality of collector routetemplates each comprise a different sensor collection routine; providinga data acquisition circuit structured to interpret a plurality ofdetection values, each of the plurality of detection valuescorresponding to at least one of the input channels. In embodiments, thedata acquisition circuit acquires sensor data from a first route ofinput channels; and providing a data analysis circuit structured toevaluate the sensor data with respect to stored anticipated stateinformation. In embodiments, the anticipated state information comprisesan alarm threshold level. In embodiments, the data analysis circuit setsan alarm state when the alarm threshold level is exceeded for a firstinput channel in the first group of input channels. In embodiments, thedata collector is configured to switch from a current routing templatecollection routine to an alternate routing template collection routinebased on a setting of an alarm state. In embodiments, the instructionsmay be deployed locally on the data collector, deployed in part locallyon the data collector and in part on a remote information technologyinfrastructure component apart from the collector. In embodiments, eachof the input channels correspond to a sensor located in the environment.

Methods and systems are disclosed herein for a system for datacollection in an industrial environment using intelligent management ofdata collection bands, referred to herein in some cases as smart bands.Smart bands may facilitate intelligent, situational, context-awarecollection of data, such as by a data collector (such as any of the widerange of data collector embodiments described throughout thisdisclosure). Intelligent management of data collection via smart bandsmay improve various parameters of data collection, as well as parametersof the processes, applications, and products that depend on datacollection, such as data quality parameters, consistency parameters,efficiency parameters, comprehensiveness parameters, reliabilityparameters, effectiveness parameters, storage utilization parameters,yield parameters (including financial yield, output yield, and reductionof adverse events), energy consumption parameters, bandwidth utilizationparameters, input/output speed parameters, redundancy parameters,security parameters, safety parameters, interference parameters,signal-to-noise parameters, statistical relevancy parameters, andothers. Intelligent management of smart bands may optimize across one ormore such parameters, such as based on a weighting of the value of theparameters; for example, a smart band may be managed to provide a givenlevel of redundancy for critical data, while not exceeding a specifiedlevel of energy usage. This may include using a variety of optimizationtechniques described throughout this disclosure and the documentsincorporated herein by reference.

In embodiments, such methods and systems for intelligent management ofsmart bands include an expert system and supporting technologycomponents, services, processes, modules, applications and interfaces,for managing the smart bands (collectively referred to in some cases asa smart band platform 10722), which may include a model-based expertsystem, a rule-based expert system, an expert system using artificialintelligence (such as a machine learning system, which may include aneural net expert system, a self-organizing map system, ahuman-supervised machine learning system, a state determination system,a classification system, or other artificial intelligence system), orvarious hybrids or combinations of any of the above. References to anexpert system should be understood to encompass utilization of any oneof the foregoing or suitable combinations, except where contextindicates otherwise. Intelligent management may be of data collection ofvarious types of data (e.g., vibration data, noise data and other sensordata of the types described throughout this disclosure) for eventdetection, state detection, and the like. Intelligent management mayinclude managing a plurality of smart bands each directed at supportingan identified application, process or workflow, such as confirmingprogress toward or alignment with one or more objectives, goals, rules,policies, or guidelines. Intelligent management may also involvemanaging data collection bands targeted to backing out an unknownvariable based on collection of other data (such as based on a model ofthe behavior of a system that involves the variable), selectingpreferred inputs among available inputs (including specifyingcombinations, fusions, or multiplexing of inputs), and/or specifying aninput band among available input bands.

Data collection bands, or smart bands, may include any number of itemssuch as sensors, input channels, data locations, data streams, dataprotocols, data extraction techniques, data transformation techniques,data loading techniques, data types, frequency of sampling, placement ofsensors, static data points, metadata, fusion of data, multiplexing ofdata, and the like as described herein. Smart band settings, which maybe used interchangeably with smart band and data collection band, maydescribe the configuration and makeup of the smart band, such as byspecifying the parameters that define the smart band. For example, datacollection bands, or smart bands, may include one or more frequencies tomeasure. Frequency data may further include at least one of a group ofspectral peaks, a true-peak level, a crest factor derived from a timewaveform, and an overall waveform derived from a vibration envelope, aswell as other signal characteristics described throughout thisdisclosure. Smart bands may include sensors measuring or data regardingone or more wavelengths, one or more spectra, and/or one or more typesof data from various sensors and metadata. Smart bands may include oneor more sensors or types of sensors of a wide range of types, such asdescribed throughout this disclosure and the documents incorporated byreference herein. Indeed, the sensors described herein may be used inany of the methods or systems described throughout this disclosure. Forexample, one sensor may be an accelerometer, such as one that measuresvoltage per G (“V/G”) of acceleration (e.g., 100 mV/G, 500 mV/G, 1 V/G,5 V/G, 10 V/G, and the like). In embodiments, the data collection bandcircuit may alter the makeup of the subset of the plurality of sensorsused in a smart band based on optimizing the responsiveness of thesensor, such as for example choosing an accelerometer better suited formeasuring acceleration of a low speed mixer versus one better suited formeasuring acceleration of a high speed industrial centrifuge. Choosingmay be done intelligently, such as for example with a proximity probeand multiple accelerometers disposed on a centrifuge where while at lowspeed, one accelerometer is used for measuring in the smart band andanother is used at high speeds. Accelerometers come in various types,such as piezo-electric crystal, low frequency (e.g., 10 V/G), high speedcompressors (10 MV/G), MEMS, and the like. In another example, onesensor may be a proximity probe which can be used for sleeve or tilt-padbearings (e.g., oil bath), or a velocity probe. In yet another example,one sensor may be a solid-state relay (SSR) that is structured toautomatically interface with a routed data collector (such as a mobileor portable data collector) to obtain or deliver data. In anotherexample, a mobile or portable data collector may be routed to alter themakeup of the plurality of available sensors, such as by bringing anappropriate accelerometer to a point of sensing, such as on or near acomponent of a machine. In still another example, one sensor may be atriax probe (e.g., a 100 MV/G triax probe), that in embodiments is usedfor portable data collection. In some embodiments, of a triax probe, avertical element on one axis of the probe may have a high frequencyresponse while the ones mounted horizontally may influence the frequencyresponse of the whole triax. In another example, one sensor may be atemperature sensor and may include a probe with a temperature sensorbuilt inside, such as to obtain a bearing temperature. In stilladditional examples, sensors may be ultrasonic, microphone, touch,capacitive, vibration, acoustic, pressure, strain gauges, thermographic(e.g., camera), imaging (e.g., camera, laser, IR, structured light), afield detector, an EMF meter to measure an AC electromagnetic field, agaussmeter, a motion detector, a chemical detector, a gas detector, aCBRNE detector, a vibration transducer, a magnetometer, positional,location-based, a velocity sensor, a displacement sensor, a tachometer,a flow sensor, a level sensor, a proximity sensor, a pH sensor, ahygrometer/moisture sensor, a densitometric sensor, an anemometer, aviscometer, or any analog industrial sensor and/or digital industrialsensor. In a further example, sensors may be directed at detecting ormeasuring ambient noise, such as a sound sensor or microphone, anultrasound sensor, an acoustic wave sensor, and an optical vibrationsensor (e.g., using a camera to see oscillations that produce noise). Instill another example, one sensor may be a motion detector.

Data collection bands, or smart bands, may be of or may be configured toencompass one or more frequencies, wavelengths, or spectra forparticular sensors, for particular groups of sensors, or for combinedsignals from multiple sensors (such as involving multiplexing or sensorfusion).

Data collection bands, or smart bands, may be of or may be configured toencompass one or more sensors or sensor data (including groups ofsensors and combined signals) from one or more pieces ofequipment/components, areas of an installation, disparate butinterconnected areas of an installation (e.g., a machine assembly lineand a boiler room used to power the line), or locations (e.g., abuilding in Cambridge and a building in Boston). Smart band settings,configurations, instructions, or specifications (collectively referredto herein using any one of those terms) may include where to place asensor, how frequently to sample a data point or points, the granularityat which a sample is taken (e.g., a number of sampling points perfraction of a second), which sensor of a set of redundant sensors tosample, an average sampling protocol for redundant sensors, and anyother aspect that would affect data acquisition.

Within the smart band platform 10722, an expert system, which maycomprise a neural net, a model-based system, a rule-based system, amachine learning data analysis circuit, and/or a hybrid of any of those,may begin iteration towards convergence on a smart band that isoptimized for a particular goal or outcome, such as predicting andmanaging performance, health, or other characteristics of a piece ofequipment, a component, or a system of equipment or components. Based oncontinuous or periodic analysis of sensor data, as patterns/trends areidentified, or outliers appear, or a group of sensor readings begin tochange, etc., the expert system may modify its data collection bandsintelligently. This may occur by triggering a rule that reflects a modelor understanding of system behavior (e.g., recognizing a shift inoperating mode that calls for different sensors as velocity of a shaftincreases) or it may occur under control of a neural net (either incombination with a rule-based approach or on its own), where inputs areprovided such that the neural net over time learns to select appropriatecollection modes based on feedback as to successful outcomes (e.g.,successful classification of the state of a system, successfulprediction, successful operation relative to a metric, or the like). Forexample, when a new pressure reactor is installed in a chemicalprocessing facility, data from the current data collection band may notaccurately predict the state or metric of operation of the system, thus,the machine learning data analysis circuit may begin to iterate todetermine if a new data collection band is better at predicting a state.Based on offset system data, such as from a library or other datastructure, certain sensors, frequency bands or other smart band membersmay be used in the smart band initially and data may be collected toassess performance. As the neural net iterates, other sensors/frequencybands may be accessed to determine their relative weight in identifyingperformance metrics. Over time, a new frequency band may be identified(or a new collection of sensors, a new set of configurations forsensors, or the like) as a better gauge of performance in the system andthe expert system may modify its data collection band based on thisiteration. For example, perhaps a slightly different or older associatedturbine agitator in a chemical reaction facility dampens one or morevibration frequencies while a different frequency is of higher amplitudeand present during optimal performance than what was seen in the offsetsystem. In this example, the smart band may be altered from what wassuggested by the corresponding offset system to capture the higheramplitude frequency that is present in the current system.

The expert system, in embodiments involving a neural net or othermachine learning system, may be seeded and may iterate, such as towardsconvergence on a smart band, based on feedback and operation parameters,such as described herein. Certain feedback may include utilizationmeasures, efficiency measures (e.g., power or energy utilization, use ofstorage, use of bandwidth, use of input/output use of perishablematerials, use of fuel, and/or financial efficiency), measures ofsuccess in prediction or anticipation of states (e.g., avoidance andmitigation of faults), productivity measures (e.g., workflow), yieldmeasures, and profit measures. Certain parameters may include: storageparameters (e.g., data storage, fuel storage, storage of inventory andthe like); network parameters (e.g., network bandwidth, input/outputspeeds, network utilization, network cost, network speed, networkavailability and the like); transmission parameters (e.g., quality oftransmission of data, speed of transmission of data, error rates intransmission, cost of transmission and the like); security parameters(e.g., number and/or type of exposure events; vulnerability to attack,data loss, data breach, access parameters, and the like); location andpositioning parameters (e.g., location of data collectors, location ofworkers, location of machines and equipment, location of inventoryunits, location of parts and materials, location of network accesspoints, location of ingress and egress points, location of landingpositions, location of sensor sets, location of network infrastructure,location of power sources and the like); input selection parameters,data combination parameters (e.g., for multiplexing, extraction,transformation, loading, and the like); power parameters; states (e.g.,operating modes, availability states, environmental states, fault modes,maintenance modes, anticipated states); events; and equipmentspecifications. With respect to states, operating modes may includemobility modes (direction, speed, acceleration, and the like), type ofmobility modes (e.g., rolling, flying, sliding, levitation, hovering,floating, and the like), performance modes (e.g., gears, rotationalspeeds, heat levels, assembly line speeds, voltage levels, frequencylevels, and the like), output modes, fuel conversion modes, resourceconsumption modes, and financial performance modes (e.g., yield,profitability, and the like). Availability states may refer toanticipating conditions that could cause machine to go offline orrequire backup. Environmental states may refer to ambient temperature,ambient humidity/moisture, ambient pressure, ambient wind/fluid flow,presence of pollution or contaminants, presence of interfering elements(e.g., electrical noise, vibration), power availability, and powerquality. Anticipated states may include: achieving or not achieving adesired goal, such as a specified/threshold output production rate, aspecified/threshold generation rate, an operational efficiency/failurerate, a financial efficiency/profit goal, a power efficiency/resourceutilization; an avoidance of a fault condition (e.g., overheating, slowperformance, excessive speed, excessive motion, excessivevibration/oscillation, excessive acceleration, expansion/contraction,electrical failure, running out of stored power/fuel, overpressure,excessive radiation/melt down, fire, freezing, failure of fluid flow(e.g., stuck valves, frozen fluids); mechanical failures (e.g., brokencomponent, worn component, faulty coupling, misalignment,asymmetries/deflection, damaged component (e.g., deflection, strain,stress, cracking], imbalances, collisions, jammed elements, and lost orslipping chain or belt); avoidance of a dangerous condition orcatastrophic failure; and availability (online status).

The expert system may comprise or be seeded with a model that predictsan outcome or state given a set of data (which may comprise inputs fromsensors, such as via a data collector, as well as other data, such asfrom system components, from external systems and from external datasources). For example, the model may be an operating model for anindustrial environment, machine, or workflow. In another example, themodel may be for anticipating states, for predicting fault andoptimizing maintenance, for self-organizing storage (e.g., on devices,in data pools and/or in the cloud), for optimizing data transport (suchas for optimizing network coding, network-condition-sensitive routing,and the like), for optimizing data marketplaces, and the like.

The iteration of the expert system may result in any number ofdownstream actions based on analysis of data from the smart band. In anembodiment, the expert system may determine that the system shouldeither keep or modify operational parameters, equipment or a weightingof a neural net model given a desired goal, such as aspecified/threshold output production rate, specified/thresholdgeneration rate, an operational efficiency/failure rate, a financialefficiency/profit goal, a power efficiency/resource utilization, anavoidance of a fault condition, an avoidance of a dangerous condition orcatastrophic failure, and the like. In embodiments, the adjustments maybe based on determining context of an industrial system, such asunderstanding a type of equipment, its purpose, its typical operatingmodes, the functional specifications for the equipment, the relationshipof the equipment to other features of the environment (including anyother systems that provide input to or take input from the equipment),the presence and role of operators (including humans and automatedcontrol systems), and ambient or environmental conditions. For example,in order to achieve a profit goal, a pipeline in a refinery may need tooperate for a certain amount of time a day and/or at a certain flowrate. The expert system may be seeded with a model for operation of thepipeline in a manner that results in a specified profit goal, such asindicating a given flow rate of material through the pipeline based onthe current market sale price for the material and the cost of gettingthe material into the pipeline. As it acquires data and iterates, themodel will predict whether the profit goal will be achieved given thecurrent data. Based on the results of the iteration of the expertsystem, a recommendation may be made (or a control instruction may beautomatically provided) to operate the pipeline at a higher flow rate,to keep it operational for longer or the like. Further, as the systemiterates, one or more additional sensors may be sampled in the model todetermine if their addition to the smart band would improve predicting astate. In another embodiment, the expert system may determine that thesystem should either keep or modify operational parameters, equipment ora weighting of a neural net or other model given a constraint ofoperation (e.g., meeting a required endpoint (e.g., delivery date,amount, cost, coordination with another system), operating with alimited resource (e.g., power, fuel, battery), storage (e.g., datastorage), bandwidth (e.g., local network, p2p, WAN, internet bandwidth,availability, or input/output capacity), authorization (e.g.,role-based)), a warranty limitation, a manufacturer's guideline, amaintenance guideline). For example, a constraint of operating a boilerin a refinery is that the aeration of the boiler feedwater needs to bereduced in the cycle; therefore, the boiler must coordinate with thedeaerator. In this example, the expert system is seeded with a model foroperation of the boiler in coordination with the de-aerator that resultsin a specified overall performance. As sensor data from the system isacquired, the expert system may determine that an aspect of one or bothof the boiler and aerator must be changed to continue to achieve thespecific overall performance. In a further embodiment, the expert systemmay determine that the system should either keep or modify operationalparameters, equipment or a weighting of a neural net model given anidentified choke point. In still another embodiment, the expert systemmay determine that the system should either keep or modify operationalparameters, equipment or a weighting of a neural net model given anoff-nominal operation. For example, a reciprocating compressor in arefinery that delivers gases at high pressure may be measured as havingan off-nominal operation by sensors that feed their data into an expertsystem (optionally including a neural net or other machine learningsystem). As the expert system iterates and receives the off-nominaldata, it may predict that the refinery will not achieve a specified goaland will recommend an action, such as taking the reciprocatingcompressor offline for maintenance. In another embodiment, the expertsystem may determine that the system should collect more/fewer datapoints from one or more sensors. For example, an anchor agitator in apharmaceutical processing plant may be programmed to agitate thecontents of a tank until a certain level of viscosity (e.g., as measuredin centipoise) is obtained. As the expert system collects datathroughout the run indicating an increase in viscosity, the expertsystem may recommend collecting additional data points to confirm apredicted state in the face of the increased strain on the plant systemsfrom the viscosity. In yet another embodiment, the expert system maydetermine that the system should change a data storage technique. Instill another example, the expert system may determine that the systemshould change a data presentation mode or manner.

In a further embodiment, the expert system may determine that the systemshould apply one or more filters (low pass, high pass, band pass, etc.)to collected data. In yet a further embodiment, the expert system maydetermine that the system should collect data from a new smart band/newset of sensors and/or begin measuring a new aspect that the neural netidentified itself. For example, various measurements may be made ofpaddle-type agitator mixers operating in a pharmaceutical plant, such asmixing times, temperature, homogeneous substrate distribution, heatexchange with internal structures and the tank wall or oxygen transferrate, mechanical stress, forces and torques on agitator vessels andinternal structures, and the like. Various sensor data streams may beincluded in a smart band monitoring these various aspects of thepaddle-type agitator mixer, such as a flow meter, a thermometer, andothers. As the expert system iterates, perhaps having been seeded withminimal data from during the agitator's run, a new aspect of theoperation may become apparent, such as the impact of pH on the state ofthe run. Thus, a new smart band will be identified by the expert systemthat includes sensor data from a pH meter. In yet still a furtherembodiment, the expert system may determine that the system shoulddiscontinue collection of data from a smart band, one or more sensors,or the like. In another embodiment, the expert system may determine thatthe system should initiate data collection from a new smart band, suchas a new smart band identified by the neural net itself. In yet anotherembodiment, the expert system may determine that the system shouldadjust the weights/biases of a model used by the expert system. In stillanother embodiment, the expert system may determine that the systemshould remove/re-task under-utilized equipment. For example, a pluralityof agitators working with a pump blasting liquid in a pharmaceuticalprocessing plant may be monitored during operation of the plant by theexpert system. Through iteration of the expert system seeded with datafrom a run of the plant with the agitators, the expert system maypredict that a state will be achieved even if one or more agitators aretaken out of service.

In embodiments, a monitoring system for data collection in an industrialenvironment may include a plurality of input sensors, such as any ofthose described herein, communicatively coupled to a data collectorhaving a controller. The monitoring system may include a data collectionband circuit structured to determine at least one subset of theplurality of sensors from which to process output data. The monitoringsystem may also include a machine learning data analysis circuitstructured to receive output data from the at least one subset of theplurality of sensors and learn received output data patterns indicativeof a state. In some embodiments, the data collection band circuit mayalter the at least one subset of the plurality of sensors, or an aspectthereof, based on one or more of the learned received output datapatterns and the state. In certain embodiments, the machine learningdata analysis circuit is seeded with a model that enables it to learndata patterns. The model may be a physical model, an operational model,a system model, and the like. In other embodiments, the machine learningdata analysis circuit is structured for deep learning. In embodiments,input data is fed to the circuit with no or minimal seeding and themachine learning data analysis circuit learns based on output feedback.For example, a static mixer in a chemical processing plant producingpolymers may be used to facilitate the polymerization reaction. Thestatic mixer may employ turbulent or laminar flow in its operation.Minimal data, such as heat transfer, velocity of flow out of the mixer,Reynolds number or pressure drop, acquired during the operation of thestatic mixer may be fed into the expert system which may iterate towardsa prediction based on initial feedback (e.g., viscosity of the polymer,color of the polymer, reactivity of the polymer).

There may be a balance of multiple goals/guidelines in the management ofsmart bands by the expert system. For example, a repair and maintenanceorganization (RMO) may have operating parameters designed formaintenance of a storage tank in a refinery, while the owner of therefinery may have particular operating parameters for the storage tankthat are designed for meeting a production goal. These goals, in thisexample relating to a maintenance goal or a production output, may betracked by a different data collection band. For example, maintenance ofa storage tank may be tracked by sensors including a vibrationtransducer and a strain gauge, while the production goal of a storagetank may be tracked by sensors including a temperature sensor and a flowmeter. The expert system may (optionally using a neural net, machinelearning system, deep learning system, or the like, which may occurunder supervision by one or more supervisors (human or automated))intelligently manage bands aligned with different goals and assignweights, parameter modifications, or recommendations based on a factor,such as a bias towards one goal or a compromise to allow betteralignment with all goals being tracked, for example. Compromises amongthe goals delivered to the expert system may be based on one or morehierarchies or rules (relating to the authority, role, criticality, orthe like) of the applicable goals. In embodiments, compromises amonggoals may be optimized using machine learning, such as a neural net,deep learning system, or other artificial intelligence system asdescribed throughout this disclosure.

In one illustrative example, in a chemical processing plant where agas-powered agitator is operating, the expert system may manage multiplesmart bands, such as one directed to detecting the operational status ofthe gas-powered agitator, one directed at identifying a probability ofhitting a production goal, and one directed at determining if theoperation of the gas-powered agitator is meeting a fuel efficiency goal.Each of these smart bands may be populated with different sensors ordata from different sensors (e.g., a vibration transducer to indicateoperational status, a flow meter to indicate production goal, and a fuelgauge to indicate a fuel efficiency) whose output data are indicative ofan aspect of the particular goal. Where a single sensor or a set ofsensors is helpful for more than one goal, overlapping smart bands(having some sensors in common and other sensors not in common) may takeinput from that sensor or set of sensors, as managed by the smart bandplatform 10722. If there are constraints on data collection (such as dueto power limitations, storage limitations, bandwidth limitations,input/output processing capabilities, or the like), a rule may indicatethat one goal (e.g., a fuel utilization goal or a pollution reductiongoal that is mandated by law or regulation) takes precedence, such thatthe data collection for the smart bands associated with that goal aremaintained as others are paused or shut down. Management ofprioritization of goals may be hierarchical or may occur by machinelearning. The expert system may be seeded with models, or may not beseeded at all, in iterating towards a predicted state (i.e., meeting thegoal) given the current data it has acquired. In this example, duringoperation of the gas-powered agitator, the plant owner may decide tobias the system towards fuel efficiency. All of the bands may still bemonitored, but as the expert system iterates and predicts that thesystem will not meet or is not meeting a particular goal, and thenoffers recommended changes directed at increasing the chance of meetingthe goal, the plant owner may structure the system with a bias towardsfuel efficiency so that the recommended changes to parameters affectingfuel efficiency are made in favor of making other recommended changes.

In embodiments, the expert system may continue iterating in adeep-learning fashion to arrive at a single smart band, after beingseeded with more than one smart band, that optimizes meeting more thanone goal. For example, there may be multiple goals tracked for a thermicheating system in a chemical processing or a food processing plant, suchas thermal efficiency and economic efficiency. Thermal efficiency forthe thermic heating system may be expressed by comparing BTUs put in tothe system, which can be obtained by knowing the amount of and qualityof the fuel being used, and the BTUs out of the system, which iscalculated using the flow out of the system and the temperaturedifferential of materials in and out of the system. Economic efficiencyof the thermic heating system may be expressed as the ratio betweencosts to run the system (including fuel, labor, materials, and services)and energy output from the system for a period of time. Data used totrack thermal efficiency may include data from a flow meter, qualitydata point(s), and a thermometer, and data used to track economicefficiency may be an energy output from the system (e.g., kWh) and costsdata. These data may be used in smart bands by the expert system topredict states, however, the expert system may iterate toward a smartband that is optimized to predict states related to both thermal andeconomic efficiency. The new smart band may include data used previouslyin the individual smart bands but may also use new data from differentsensors or data sources. In embodiments, the expert system may be seededwith a plurality of smart bands and iterate to predict various states,but may also iterate towards reducing the number of smart bands neededto predict the same set of states.

Iteration of the expert system may be governed by rules, in someembodiments. For example, the expert system may be structured to collectdata for seeding at a pre-determined frequency. The expert system may bestructured to iterate at least a number of times, such as when a newcomponent/equipment/fuel source is added, when a sensor goes off-line,or as standard practice. For example, when a sensor measuring therotation of a stirrer in a food processing line goes off-line and theexpert system begins acquiring data from a new sensor measuring the samedata points, the expert system may be structured to iterate for a numberof times before the state is utilized in or allowed to affect anydownstream actions. The expert system may be structured to trainoff-line or train in situ/online. The expert system may be structured toinclude static and/or manually input data in its smart bands. Forexample, an expert system managing smart bands associated with a mixerin a food processing plant may be structured to iterate towardspredicting a duration of mixing before the food being processed achievesa particular viscosity. In embodiments, the smart band includes dataregarding the speed of the mixer, temperature of its contents,viscometric measurements and the required endpoint for viscosity andtemperature of the food. The expert system may be structured to includea minimum/maximum number of variables.

In embodiments, the expert system may be overruled. In embodiments, theexpert system may revert to prior band settings, such as in the eventthe expert system fails, such as if a neural network fails in a neuralnet expert system, if uncertainty is too high in a model-based system,if the system is unable to resolve conflicting rules in rule-basedsystem, or the system cannot converge on a solution in any of theforegoing. For example, sensor data on an irrigation system used by theexpert system in a smart band may indicate a massive leak in the field,but visual inspection, such as by a drone, indicates no such leak. Inthis event, the expert system will revert to an original smart band forseeding the expert system. In another example, one or more point sensorson an industrial pressure cooker indicates imminent failure in a seal,but the data collection band that the expert system converged to with aweighting towards a performance metric did not identify the failure. Inthis event, the smart band will revert to an original setting or aversion of the smart band that would have also identified the imminentfailure of the pressure cooker seal. In embodiments, the expert systemmay change smart band settings in the event that a new component isadded that makes the system closer to a different offset system. Forexample, a vacuum distillation unit is added to an oil & gas refinery todistill naphthalene, but the current smart band settings for the expertsystem are derived from a refinery that distills kerosene. In thisexample, a data structure with smart band settings for various offsetsystems may be searched for a system that is more closely matched to thecurrent system. When a new offset system is identified as more closelymatched, such as one that also distill naphthalene, the new smart bandsettings (e.g., which sensors to use, where to place them, howfrequently to sample, what static data points are needed, etc. asdescribed herein) are used to seed the expert system to iterate towardspredicting a state for the system. In embodiments, the expert system maychange smart band settings in the event that a new set of offset data isavailable from a third-party library. For example, a pharmaceuticalprocessing plant may have optimized a catalytic reactor to operate in ahighly efficient way and deposited the smart band settings in a datastructure. The data structure may be continuously scanned for new smartbands that better aid in monitoring catalytic reactions and thus, resultin optimizing the operation of the reactor.

In embodiments, the expert system may be used to uncover unknownvariables. For example, the expert system may iterate to identify amissing variable to be used for further iterations, such as furtherneural net iterations. For example, an under-utilized tank in a legacycondensate/make-up water system of a power station may have an unknowncapacity because it is inaccessible and no documentation exists on thetank. Various aspects of the tank may be measured by a swarm of sensorsto arrive at an estimated volume (e.g., flow into a downstream space,duration of a dye traced solution to work through the system), then thatvolume can be fed into the neural net as a new variable in the smartband.

In embodiments, the location of expert system node locations may be on amachine, on a data collector (or a group of them), in a networkinfrastructure (enterprise or other), or in the cloud. In embodiments,there may be distributed neurons across nodes (e.g., machine, datacollector, network, cloud).

In an aspect, a monitoring system 10700 for data collection in anindustrial environment, comprising a plurality of input sensors 10702communicatively coupled to the data collector 10704 having a controller10706, a data collection band circuit 10708 structured to determine atleast one collection parameter for at least one of the plurality ofsensors 10702 from which to process output data 10710, and the machinelearning data analysis circuit 10712 structured to receive the outputdata 10710 from the at least one of the plurality of sensors 10702 andlearn received output data patterns 10718 indicative of a state. Thedata collection band circuit 10708 alters the at least one collectionparameter for the at least one of the plurality of sensors 10702 basedon one or more of the learned received output data patterns 10718 andthe state. The state may correspond to an outcome relating to a machinein the environment, an anticipated outcome relating to a machine in theenvironment, an outcome relating to a process in the environment, ananticipated outcome relating to a process in the environment, and thelike. The collection parameter may be a bandwidth parameter, may be usedto govern the multiplexing of a plurality of the input sensors, may be atiming parameter, may relate to a frequency range, may relate to thegranularity of collection of sensor data, is a storage parameter for thecollected data. The machine learning data analysis circuit may bestructured to learn received output data patterns 10718 by being seededwith a model 10720, which may be a physical model, an operational model,or a system model. The machine learning data analysis circuit may bestructured to learn received output data patterns 10718 based on thestate. The data collection band circuit may alter the subset of theplurality of sensors when the learned received output data pattern doesnot reliably predict the state, which may include discontinuingcollection of data from the at least one subset.

The monitoring system 10700 may keep or modify operational parameters ofan item of equipment in the environment based on the determined state.The controller 10706 may adjust the weighting of the machine learningdata analysis circuit 10712 based on the learned received output datapatterns 10718 or the state. The controller 10706 may collect more/fewerdata points from one or more members of the at least one subset of theplurality of sensors 10702 based on the learned received output datapatterns 10718 or the state. The controller 10706 may change a datastorage technique for the output data 10710 based on the learnedreceived output data patterns 10718 or the state. The controller 10706may change a data presentation mode or manner based on the learnedreceived output data patterns 10718 or the state. The controller 10706may apply one or more filters to the output data 10710. The controller10706 may identify a new data collection band circuit 10708 based on oneor more of the learned received output data patterns 10718 and thestate. The controller 10706 may adjust the weights/biases of the machinelearning data analysis circuit 10712, such as in response to the learnedreceived output data patterns 10718, in response to the accuracy of theprediction of an anticipated state by the machine learning data analysiscircuit, in response to the accuracy of a classification of a state bythe machine learning data analysis circuit, and the like. The monitoringdevice 10700 may remove or re-task under-utilized equipment based on oneor more of the learned received output data patterns 10718 and thestate. The machine learning data analysis circuit 10712 may include aneural network expert system. At least one subset of the plurality ofsensors measures vibration and noise data. The machine learning dataanalysis circuit 10712 may be structured to learn received output datapatterns 10718 indicative of progress/alignment with one or moregoals/guidelines. In embodiments, progress/alignment of eachgoal/guideline may be determined by a different subset of the pluralityof sensors. The machine learning data analysis circuit 10712 may bestructured to learn received output data patterns 10718 indicative of anunknown variable. The machine learning data analysis circuit 10712 maybe structured to learn received output data patterns 10718 indicative ofa preferred input among available inputs. The machine learning dataanalysis circuit 10712 may be structured to learn received output datapatterns 10718 indicative of a preferred input data collection bandamong available input data collection bands. The machine learning dataanalysis circuit 10712 may be disposed in part on a machine, on one ormore data collectors, in network infrastructure, in the cloud, or anycombination thereof.

In embodiments, a monitoring device for data collection in an industrialenvironment may include the plurality of input sensors 10702communicatively coupled to the controller 10706, the controller 10706including the data collection band circuit 10708 structured to determineat least one subset of the plurality of sensors 10702 from which toprocess the output data 10710; and a machine learning data analysiscircuit 10712 structured to receive output data from the at least onesubset of the plurality of sensors 10702 and learn received output datapatterns 10718 indicative of a state. In embodiments, the datacollection band circuit 10708 alters an aspect of the at least onesubset of the plurality of sensors 10702 based on one or more of thelearned received output data patterns 10718 and the state. The aspectthat the data collection band circuit 10708 alters is a number or afrequency of data points collected from one or more members of the atleast one subset of the plurality of sensors 10702. The aspect that thedata collection band circuit 10708 alters is a bandwidth parameter, atiming parameter, a frequency range, a granularity of collection ofsensor data, a storage parameter for the collected data, and the like.

In an embodiment, the monitoring system 10700 for data collection in anindustrial environment may include the plurality of input sensors 10702communicatively coupled to the data collector 10704 having thecontroller 10706, the data collection band circuit 10708 structured todetermine at least one collection parameter for at least one of theplurality of sensors 10702 from which to process the output data 10710,and a machine learning data analysis circuit 10712 structured to receivethe output data 10710 from the at least one of the plurality of sensors10702 and learn received output data patterns indicative of a state. Inembodiments, the data collection band circuit 10708 alters the at leastone collection parameter for the at least one of the plurality ofsensors 10702 based on one or more of the learned received output datapatterns 10718 and the state. In embodiments, the data collection bandcircuit 10708 alters the at least one of the plurality of sensors 10702when the learned received output data pattern 10718 does not reliablypredict the state.

In an embodiment, the monitoring system 10700 for data collection in anindustrial environment may include the plurality of input sensors 10702communicatively coupled to the data collector 10704 having thecontroller 10706, the data collection band circuit 10708 structured todetermine at least one collection parameter for at least one of theplurality of sensors 10702 from which to process the output data 10710,and the machine learning data analysis circuit 10712 structured toreceive the output data 10710 from the at least one of the plurality ofsensors 10702 and learn received output data patterns 10718 indicativeof a state. In embodiments, the data collection band circuit 10708alters the at least one collection parameter for the at least one of theplurality of sensors 10702 based on one or more of the learned receivedoutput data patterns 10718 and the state. In embodiments, the datacollector 10704 collects more or fewer data points from the at least oneof the plurality of sensors 10702 based on the learned received outputdata patterns 10718 or the state.

In an embodiment, the monitoring system 10700 for data collection in anindustrial environment may include the plurality of input sensors 10702communicatively coupled to the data collector 10704 having thecontroller 10706, the data collection band circuit 10708 structured todetermine at least one collection parameter for at least one of theplurality of sensors 10702 from which to process the output data 10710,and the machine learning data analysis circuit 10712 structured toreceive the output data 10710 from the at least one of the plurality ofsensors 10702 and learn received the output data 10710 patternsindicative of a state. In embodiments, the data collection band circuit10708 alters the at least one collection parameter for the at least oneof the plurality of sensors 10702 based on one or more of the learnedreceived output data patterns 10718 and the state. In embodiments, thecontroller 10706 changes a data storage technique for the output data10710 based on the learned received output data patterns 10718 or thestate.

In an embodiment, the monitoring system 10700 for data collection in anindustrial environment may include the plurality of input sensors 10702communicatively coupled to the data collector 10704 having thecontroller 10706, the data collection band circuit 10708 structured todetermine at least one collection parameter for at least one of theplurality of sensors 10702 from which to process the output data 10710,and a machine learning data analysis circuit 10712 structured to receivethe output data 10710 from the at least one of the plurality of sensors10702 and learn received output data patterns 10718 indicative of astate. In embodiments, the data collection band circuit 10708 alters theat least one collection parameter for the at least one of the pluralityof sensors 10702 based on one or more of the learned received outputdata patterns 10718 and the state. In embodiments, the controller 10706changes a data presentation mode or manner based on the learned receivedoutput data patterns 10718 or the state.

In an embodiment, the monitoring system 10700 for data collection in anindustrial environment may include the plurality of input sensors 10702communicatively coupled to the data collector 10704 having thecontroller 10706, the data collection band circuit 10708 structured todetermine at least one collection parameter for at least one of theplurality of sensors 10702 from which to process the output data 10710,and a machine learning data analysis circuit 10712 structured to receivethe output data 10710 from the at least one of the plurality of sensors10702 and learn received output data patterns 10718 indicative of astate. In embodiments, the data collection band circuit 10708 alters theat least one collection parameter for the at least one of the pluralityof sensors 10702 based on one or more of the learned received outputdata patterns 10718 and the state. In embodiments, the controller 10706identifies a new data collection band circuit 10708 based on one or moreof the learned received output data patterns 10718 and the state.

In an embodiment, the monitoring system 10700 for data collection in anindustrial environment may include the plurality of input sensors 10702communicatively coupled to the data collector 10704 having thecontroller 10706, the data collection band circuit 10708 structured todetermine at least one collection parameter for at least one of theplurality of sensors 10702 from which to process the output data 10710,and the machine learning data analysis circuit 10712 structured toreceive the output data 10710 from the at least one of the plurality ofsensors 10702 and learn received output data patterns 10718 indicativeof a state. In embodiments, the data collection band circuit 10708alters the at least one collection parameter for the at least one of theplurality of sensors 10702 based on one or more of the learned receivedoutput data patterns 10718 and the state. In embodiments, the controller10706 adjusts the weights/biases of the machine learning data analysiscircuit 10712. The adjustment may be in response to the learned receivedoutput data patterns, in response to the accuracy of the prediction ofan anticipated state by the machine learning data analysis circuit, inresponse to the accuracy of a classification of a state by the machinelearning data analysis circuit, and the like.

In an embodiment, the monitoring system 10700 for data collection in anindustrial environment may include the plurality of input sensors 10702communicatively coupled to the data collector 10704 having thecontroller 10706, the data collection band circuit 10708 structured todetermine at least one collection parameter for at least one of theplurality of sensors 10702 from which to process the output data 10710,and a machine learning data analysis circuit 10712. This machinelearning data analysis circuit is structured to receive the output data10710 from the at least one of the plurality of sensors 10702 and learnreceived output data patterns 10718 indicative of a state. Inembodiments, the data collection band circuit 10708 alters the at leastone collection parameter for the at least one of the plurality ofsensors 10702 based on one or more of the learned received output datapatterns 10718 and the state. In embodiments, the machine learning dataanalysis circuit 10712 is structured to learn received output datapatterns 10718 indicative of progress or alignment with one or moregoals or guidelines.

Claim 1. In embodiments, a monitoring system for data collection in anindustrial environment, comprising: a plurality of input sensorscommunicatively coupled to a data collector having a controller; a datacollection band circuit structured to determine at least one collectionparameter for at least one of the plurality of sensors from which toprocess output data; and a machine learning data analysis circuitstructured to receive output data from the at least one of the pluralityof sensors and learn received output data patterns indicative of astate. In embodiments, the data collection band circuit alters the atleast one collection parameter for the at least one of the plurality ofsensors based on one or more of the learned received output datapatterns and the state. In embodiments, the state corresponds to anoutcome relating to a machine in the environment. In embodiments, thestate corresponds to an anticipated outcome relating to a machine in theenvironment. In embodiments, the state corresponds to an outcomerelating to a process in the environment. In embodiments, the statecorresponds to an anticipated outcome relating to a process in theenvironment. In embodiments, the collection parameter is a bandwidthparameter. In embodiments, the collection parameter is used to governthe multiplexing of a plurality of the input sensors. In embodiments,the collection parameter is a timing parameter. In embodiments, thecollection parameter relates to a frequency range. In embodiments, thecollection parameter relates to the granularity of collection of sensordata. In embodiments, the collection parameter is a storage parameterfor the collected data. In embodiments, the machine learning dataanalysis circuit is structured to learn received output data patterns bybeing seeded with a model. In embodiments, the model is a physicalmodel, an operational model, or a system model. In embodiments, themachine learning data analysis circuit is structured to learn receivedoutput data patterns based on the state. In embodiments, the datacollection band circuit alters the subset of the plurality of sensorswhen the learned received output data pattern does not reliably predictthe state. In embodiments, altering at least one subset comprisesdiscontinuing collection of data from the at least one subset. Inembodiments, the monitoring system keeps or modifies operationalparameters of an item of equipment in the environment based on thedetermined state. In embodiments, the controller adjusts the weightingof the machine learning data analysis circuit based on the learnedreceived output data patterns or the state. In embodiments, thecontroller collects more or fewer data points from one or more membersof the at least one subset of plurality of sensors based on the learnedreceived output data patterns or the state. In embodiments, thecontroller changes a data storage technique for the output data based onthe learned received output data patterns or the state. In embodiments,the controller changes a data presentation mode or manner based on thelearned received output data patterns or the state. In embodiments, thecontroller applies one or more filters to the output data. Inembodiments, the controller identifies a new data collection bandcircuit based on one or more of the learned received output datapatterns and the state. In embodiments, the controller adjusts theweights/biases of the machine learning data analysis circuit. Inembodiments, the adjustment is in response to the learned receivedoutput data patterns. In embodiments, the adjustment is in response tothe accuracy of the prediction of an anticipated state by the machinelearning data analysis circuit. In embodiments, the adjustment is inresponse to the accuracy of a classification of a state by the machinelearning data analysis circuit. In embodiments, the monitoring deviceremoves/re-tasks under-utilized equipment based on one or more of thelearned received output data patterns and the state. In embodiments, themachine learning data analysis circuit comprises a neural network expertsystem. In embodiments, the at least one subset of the plurality ofsensors measure vibration and noise data. In embodiments, the machinelearning data analysis circuit is structured to learn received outputdata patterns indicative of progress/alignment with one or moregoals/guidelines. In embodiments, progress/alignment of eachgoal/guideline is determined by a different subset of the plurality ofsensors. In embodiments, the machine learning data analysis circuit isstructured to learn received output data patterns indicative of anunknown variable. In embodiments, the machine learning data analysiscircuit is structured to learn received output data patterns indicativeof a preferred input among available inputs. In embodiments, the machinelearning data analysis circuit is structured to learn received outputdata patterns indicative of a preferred input data collection band amongavailable input data collection bands. In embodiments, the machinelearning data analysis circuit is disposed in part on a machine, on oneor more data collectors, in network infrastructure, in the cloud, or anycombination thereof.

In embodiments, methods and systems include a monitoring device for datacollection in an industrial environment including a plurality of inputsensors communicatively coupled to a controller, the controllercomprising: a data collection band circuit structured to determine atleast one subset of the plurality of sensors from which to processoutput data; and a machine learning data analysis circuit structured toreceive output data from the at least one subset of the plurality ofsensors and learn received output data patterns indicative of a state.In embodiments, the data collection band circuit alters an aspect of theat least one subset of the plurality of sensors based on one or more ofthe learned received output data patterns and the state. In embodiments,the aspect that the data collection band circuit alters is a number ofdata points collected from one or more members of the at least onesubset of plurality of sensors. In embodiments, the aspect that the datacollection band circuit alters is a frequency of data points collectedfrom one or more members of the at least one subset of plurality ofsensors. In embodiments, the aspect that the data collection bandcircuit alters is a bandwidth parameter. In embodiments, the aspect thatthe data collection band circuit alters is a timing parameter. Inembodiments, the aspect that the data collection band circuit altersrelates to a frequency range. In embodiments, the aspect that the datacollection band circuit alters relates to the granularity of collectionof sensor data. In embodiments, the collection parameter is a storageparameter for the collected data. In embodiments, methods and systeminclude a monitoring system for data collection in an industrialenvironment. The system includes a plurality of input sensorscommunicatively coupled to a data collector having a controller; a datacollection band circuit structured to determine at least one collectionparameter for at least one of the plurality of sensors from which toprocess output data; and a machine learning data analysis circuitstructured to receive output data from the at least one of the pluralityof sensors and learn received output data patterns indicative of astate. In embodiments, the data collection band circuit alters the atleast one collection parameter for the at least one of the plurality ofsensors based on one or more of the learned received output datapatterns and the state. In embodiments, the data collection band circuitalters the at least one of the plurality of sensors when the learnedreceived output data pattern does not reliably predict the state.

In embodiments, methods and system disclosed herein include a monitoringsystem for data collection in an industrial environment. The systemincludes a plurality of input sensors communicatively coupled to a datacollector having a controller; a data collection band circuit structuredto determine at least one collection parameter for at least one of theplurality of sensors from which to process output data; and a machinelearning data analysis circuit structured to receive output data fromthe at least one of the plurality of sensors and learn received outputdata patterns indicative of a state. In embodiments, the data collectionband circuit alters the at least one collection parameter for the atleast one of the plurality of sensors based on one or more of thelearned received output data patterns and the state. In embodiments, thedata collector collects more or fewer data points from the at least oneof the plurality of sensors based on the learned received output datapatterns or the state.

In embodiments, methods and systems disclosed herein include amonitoring system for data collection in an industrial environment,comprising: a plurality of input sensors communicatively coupled to adata collector having a controller; a data collection band circuitstructured to determine at least one collection parameter for at leastone of the plurality of sensors from which to process output data; and amachine learning data analysis circuit structured to receive output datafrom the at least one of the plurality of sensors and learn receivedoutput data patterns indicative of a state. In embodiments, the datacollection band circuit alters the at least one collection parameter forthe at least one of the plurality of sensors based on one or more of thelearned received output data patterns and the state. In embodiments, thecontroller changes a data storage technique for the output data based onthe learned received output data patterns or the state.

In embodiments, methods and systems disclosed herein include amonitoring system for data collection in an industrial environment. Thesystem includes a plurality of input sensors communicatively coupled toa data collector having a controller; a data collection band circuitstructured to determine at least one collection parameter for at leastone of the plurality of sensors from which to process output data; and amachine learning data analysis circuit structured to receive output datafrom the at least one of the plurality of sensors and learn receivedoutput data patterns indicative of a state. In embodiments, the datacollection band circuit alters the at least one collection parameter forthe at least one of the plurality of sensors based on one or more of thelearned received output data patterns and the state. In embodiments, thecontroller changes a data presentation mode or manner based on thelearned received output data patterns or the state.

In embodiments, methods and systems disclosed herein include amonitoring system for data collection in an industrial environment. Thesystem includes a plurality of input sensors communicatively coupled toa data collector having a controller; a data collection band circuitstructured to determine at least one collection parameter for at leastone of the plurality of sensors from which to process output data; and amachine learning data analysis circuit structured to receive output datafrom the at least one of the plurality of sensors and learn receivedoutput data patterns indicative of a state. In embodiments, the datacollection band circuit alters the at least one collection parameter forthe at least one of the plurality of sensors based on one or more of thelearned received output data patterns and the state. In embodiments, thecontroller identifies a new data collection band circuit based on one ormore of the learned received output data patterns and the state.

In embodiments, methods and systems include a monitoring system for datacollection in an industrial environment. The system includes a pluralityof input sensors communicatively coupled to a data collector having acontroller; a data collection band circuit structured to determine atleast one collection parameter for at least one of the plurality ofsensors from which to process output data; and a machine learning dataanalysis circuit structured to receive output data from the at least oneof the plurality of sensors and learn received output data patternsindicative of a state. In embodiments, the data collection band circuitalters the at least one collection parameter for the at least one of theplurality of sensors based on one or more of the learned received outputdata patterns and the state. In embodiments, the controller adjusts theweights/biases of the machine learning data analysis circuit. Inembodiments, the adjustment is in response to the learned receivedoutput data patterns. In embodiments, the adjustment is in response tothe accuracy of the prediction of an anticipated state by the machinelearning data analysis circuit. In embodiments, the adjustment is inresponse to the accuracy of a classification of a state by the machinelearning data analysis circuit.

In embodiments, methods and systems disclosed herein include amonitoring system for data collection in an industrial environment. Thesystem includes a plurality of input sensors communicatively coupled toa data collector having a controller; a data collection band circuitstructured to determine at least one collection parameter for at leastone of the plurality of sensors from which to process output data; and amachine learning data analysis circuit structured to receive output datafrom the at least one of the plurality of sensors and learn receivedoutput data patterns indicative of a state. In embodiments, the datacollection band circuit alters the at least one collection parameter forthe at least one of the plurality of sensors based on one or more of thelearned received output data patterns and the state. In embodiments, themachine learning data analysis circuit is structured to learn receivedoutput data patterns indicative of progress or alignment with one ormore goals or guidelines.

As described elsewhere herein, an expert system in an industrialenvironment may use sensor data to make predictions about outcomes orstates of the environment or items in the environment. Data collectionmay be of various types of data (e.g., vibration data, noise data andother sensor data of the types described throughout this disclosure) forevent detection, state detection, and the like. For example, the expertsystem may utilize ambient noise, or the overall sound environment ofthe area and/or overall vibration of the device of interest, optionallyin conjunction with other sensor data, in detecting or predicting eventsor states. For example, a reciprocating compressor in a refinery, whichmay generate its own vibration, may also have an ambient vibrationthrough contact with other aspects of the system.

In embodiments, all three types of noise (ambient noise, local noise andvibration noise) including various subsets thereof and combinations withother types of data, may be organized into large data sets, along withmeasured results, that are processed by a “deep learning” machine/expertsystem that learns to predict one or more states (e.g., maintenance,failure, or operational) or overall outcomes, such as by learning fromhuman supervision or from other feedback, such as feedback from one ormore of the systems described throughout this disclosure and thedocuments incorporated by reference herein.

Throughout this disclosure, various examples will involve machines,components, equipment, assemblies, and the like, and it should beunderstood that the disclosure could apply to any of the aforementioned.Elements of these machines operating in an industrial environment (e.g.,rotating elements, reciprocating elements, swinging elements, flexingelements, flowing elements, suspending elements, floating elements,bouncing elements, bearing elements, etc.) may generate vibrations thatmay be of a specific frequency and/or amplitude typical of the elementwhen the element is in a given operating condition or state (e.g., anormal mode of operation of a machine at a given speed, in a given gear,or the like). Changes in a parameter of the vibration may be indicativeor predictive of a state or outcome of the machine. Various sensors maybe useful in measuring vibration, such as accelerometers, velocitytransducers, imaging sensors, acoustic sensors, and displacement probes,which may collectively be known as vibration sensors. Vibration sensorsmay be mounted to the machine, such as permanently or temporarily (e.g.,adhesive, hook-and-loop, or magnetic attachment), or may be disposed ona mobile or portable data collector. Sensed conditions may be comparedto historical data to identify or predict a state, condition or outcome.Typical faults that can be identified using vibration analysis include:machine out of balance, machine out of alignment, resonance, bentshafts, gear mesh disturbances, blade pass disturbances, vane passdisturbances, recirculation & cavitation, motor faults (rotor & stator),bearing failures, mechanical looseness, critical machine speeds, and thelike, as well as excessive friction, clutch slipping, belt problems,suspension and shock absorption problems, valve and other fluid leaks,under-pressure states in lubrication and other fluid systems,overheating (such as due to many of the above), blockage or freezing ofengagement of mechanical systems, interference effects, and other faultsdescribed throughout this disclosure and in the documents incorporatedby reference.

Given that machines are frequently found adjacent to or working inconcert with other machinery, measuring the vibration of the machine maybe complicated by the presence of various noise components in theenvironment or associated vibrations that the machine may be subjectedto. Indeed, the ambient and/or local environment may have its ownvibration and/or noise pattern that may be known. In embodiments, thecombination of vibration data with ambient and/or local noise or otherambient sensed conditions may form its own pattern, as will be furtherdescribed herein.

In embodiments, measuring vibration noise may involve one or morevibration sensors on or in a machine to measure vibration noise of themachine that occurs continuously or periodically. Analysis of thevibration noise may be performed, such as filtering, signalconditioning, spectral analysis, trend analysis, and the like. Analysismay be performed on aggregate or individual sensor measurements toisolate vibration noise of equipment to obtain a characteristicvibration, vibration pattern or “vibration fingerprint” of the machine.The vibration fingerprints may be stored in a data structure, orlibrary, of vibration fingerprints. The vibration fingerprints mayinclude frequencies, spectra (i.e., frequency vs. amplitude),velocities, peak locations, wave peak shapes, waveform shapes, waveenvelope shapes, accelerations, phase information, phase shifts(including complex phase measurements) and the like. Vibrationfingerprints may be stored in the library in association with aparameter by which it may be searched or sorted. The parameters mayinclude a brand or type of machine/component/equipment, location ofsensor(s) attachment or placement, duty cycle of the equipment/machine,load sharing of the equipment/machine, dynamic interactions with otherdevices, RPM, flow rate, pressure, other vibration drivingcharacteristic, voltage of line power, age of equipment, time ofoperation, known neighboring equipment, associated auxiliaryequipment/components, size of space equipment is in, material ofplatform for equipment, heat flux, magnetic fields, electrical fields,currents, voltage, capacitance, inductance, aspect of a product, andcombinations (e.g., simple ratios) of the same. Vibration fingerprintsmay be obtained for machines under normal operation or for other periodsof operation (e.g., off-nominal operation, malfunction, maintenanceneeded, faulty component, incorrect parameters of operation, otherconditions, etc.) and can be stored in the library for comparison tocurrent data. The library of vibration fingerprints may be stored asindicators with associated predictions, states, outcomes and/or events.Trend analysis data of measured vibration fingerprints can indicate timebetween maintenance events/failure events.

In embodiments, vibration noise may be used by the expert system toconfirm the status of a machine, such as a favorable operation, aproduction rate, a generation rate, an operational efficiency, afinancial efficiency (e.g., output per cost), a power efficiency, andthe like. In embodiments, the expert system may make a comparison of thevibration noise with a stored vibration fingerprint. In otherembodiments, the expert system may be seeded with vibration noise andinitial feedback on states and outcomes in order to learn to predictother states and outcomes. For example, a center pivot irrigation systemmay be remotely monitored by attached vibration sensors to provide ameasured vibration noise that can be compared to a library of vibrationfingerprints to confirm that the system is operating normally. If thesystem is not operating normally, the expert system may automaticallydispatch a field crew or drone to investigate. In another example of avacuum distillation unit in a refinery, the vibration noise may becompared, such as by the expert system, to stored vibration fingerprintsin a library to confirm a production rate of diesel. In a furtherexample, the expert system may be seeded with vibration noise for apipeline under conditions of a normal production rate and as the expertsystem iterates with current data (e.g., altered vibration noise, andpossibly other altered parameters), it may predict that the productionrate has increased as caused by the alterations. Measurements may becontinually analyzed in this way to remotely monitor operation.

In embodiments, vibration noise may be compared, such as by the expertsystem, to stored vibration fingerprints and associated states andoutcomes in the library, or alternatively, may be used to seed an expertsystem to predict when maintenance is required (e.g., off-nominalmeasurement, artifacts in signal, etc.), such as when vibration noise ismatched to a condition when the equipment/component requiredmaintenance, vibration noise exceeds a threshold/limit, vibration noiseexceeds a threshold/limit or matches a library vibration fingerprinttogether with one or more additional parameters, as described herein.For example, when the vibration fingerprint from a turbine agitator in apharmaceutical processing plant matches a vibration fingerprint for aturbine agitator when it required a replacement bearing, the expertsystem may cause an action to occur, such as immediately shutting downthe agitator or scheduling its shutdown and maintenance.

In embodiments, vibration noise may be compared, such as by the expertsystem, to stored vibration fingerprints and associated states andoutcomes in the library, or alternatively, may be used to seed an expertsystem to predict a failure or an imminent failure. For example,vibration noise from a gas agitator in a pharmaceutical processing plantmay be matched to a condition when the agitator previously failed or wasabout to fail. In this example, the expert system may immediately shutdown the agitator, schedule its shutdown, or cause a backup agitator tocome online. In another example, vibration noise from a pump blastingliquid agitator in a chemical processing plant may exceed a threshold orlimit and the expert system may cause an investigation into the cause ofthe excess vibration noise, shut down the agitator, or the like. Inanother example, vibration noise from an anchor agitator in apharmaceutical processing plant may exceed a threshold/limit or match alibrary vibration fingerprint together with one or more additionalparameters (see parameters herein), such as a decreased flow rate,increased temperature, or the like. Using vibration noise taken togetherwith the parameters, the expert system may more reliably predict thefailure or imminent failure.

In embodiments, vibration noise may be compared, such as by the expertsystem, to stored vibration fingerprints and associated states andoutcomes in the library, or alternatively, may be used to seed an expertsystem to predict or diagnose a problem (e.g., unbalanced, misaligned,worn, or damaged) with the equipment or an external source contributingvibration noise to the equipment. For example, when the vibration noisefrom a paddle-type agitator mixer matches a vibration fingerprint from aprior imbalance, the expert system may immediately shut down the mixer.

In embodiments, when the expert system makes a prediction of an outcomeor state using vibration noise, the expert system may perform adownstream action, or cause it to be performed. Downstream actions mayinclude: triggering an alert of a failure, imminent failure, ormaintenance event; shutting down equipment/component; initiatingmaintenance/lubrication/alignment; deploying a field technician;recommending a vibration absorption/dampening device; modifying aprocess to utilize backup equipment/component; modifying a process topreserve products/reactants, etc.; generating/modifying a maintenanceschedule; coupling the vibration fingerprint with duty cycle of theequipment, RPM, flow rate, pressure, temperature or othervibration-driving characteristic to obtain equipment/component statusand generate a report, and the like. For example, vibration noise for acatalytic reactor in a chemical processing plant may be matched to acondition when the catalytic reactor required maintenance. Based on thispredicted state of required maintenance, the expert system may deploy afield technician to perform the maintenance.

In embodiments, the library may be updated if a changed parameterresulted in a new vibration fingerprint, or if a predicted outcome orstate did not occur in the absence of mitigation. In embodiments, thelibrary may be updated if a vibration fingerprint was associated with analternative state than what was predicted by the library. The update mayoccur after just one time that the state that actually occurred did notmatch the predicted state from the library. In other embodiments, it mayoccur after a threshold number of times. In embodiments, the library maybe updated to apply one or more rules for comparison, such as rules thatgovern how many parameters to match along with the vibrationfingerprint, or the standard deviation for the match in order to acceptthe predicted outcome.

In embodiments, vibration noise may be compared, such as by the expertsystem, to stored vibration fingerprints and associated states andoutcomes in the library, or alternatively, may be used to seed an expertsystem to determine if a change in a system parameter external orinternal to the machine has an effect on its intrinsic operation. Inembodiments, a change in one or more of a temperature, flow rate,materials in use, duration of use, power source, installation, or otherparameter (see parameters above) may alter the vibration fingerprint ofa machine. For example, in a pressure reactor in a chemical processingplant, the flow rate and a reactant may be changed. The changes mayalter the vibration fingerprint of the machine such that the vibrationfingerprint stored in the library for normal operation is no longercorrect.

Ambient noise, or the overall sound environment of the area and/oroverall vibration of the device of interest, optionally in conjunctionwith other ambient sensed conditions, may be used in detecting orpredicting events, outcomes, or states. Ambient noise may be measured bya microphone, ultrasound sensors, acoustic wave sensors, opticalvibration sensors (e.g., using a camera to see oscillations that producenoise), or “deep learning” neural networks involving various sensorarrays that learn, using large data sets, to identify patterns, soundstypes, noise types, etc. In an embodiment, the ambient sensed conditionmay relate to motion detection. For example, the motion may be aplatform motion (e.g., vehicle, oil platform, suspended platform onland, etc.) or an object motion (e.g., moving equipment, people, robots,parts (e.g., fan blades or turbine blades), etc.). In an embodiment, theambient sensed condition may be sensed by imaging, such as to detect alocation and nature of various machines, equipment, and other objects,such as ones that might impact local vibration. In an embodiment, theambient sensed condition may be sensed by thermal detection and imaging(e.g., for presence of people; presence of heat sources that may affectperformance parameters, etc.). In an embodiment, the ambient sensedcondition may be sensed by field detection (e.g., electrical, magnetic,etc.). In an embodiment, the ambient sensed condition may be sensed bychemical detection (e.g., smoke, other conditions). Any sensor data maybe used by the expert system to provide an ambient sensed condition foranalysis along with the vibration fingerprint to predict an outcome,event, or state. For example, an ambient sensed condition near a stirreror mixer in a food processing plant may be the operation of a spaceheater during winter months. In embodiments, the ambient sensedcondition may include an ambient noise and an ambient temperature.

In an aspect, local noise may be the noise or vibration environmentwhich is ambient, but known to be locally generated. The expert systemmay filter out ambient noise, employ common mode noise removal, and/orphysically isolate the sensing environment.

In embodiments, a system for data collection in an industrialenvironment may use ambient, local and vibration noise for prediction ofoutcomes, events, and states. A library may be populated with each ofthe three noise types for various conditions (e.g., start up, shut down,normal operation, other periods of operation as described elsewhereherein). In other embodiments, the library may be populated with noisepatterns representing the aggregate ambient, local, and/or vibrationnoise. Analysis (e.g., filtering, signal conditioning, spectralanalysis, trend analysis) may be performed on the aggregate noise toobtain a characteristic noise pattern and identify changes in noisepattern as possible indicators of a changed condition. A library ofnoise patterns may be generated with established vibration fingerprintsand local and ambient noise that can be sorted by a parameter (seeparameters herein), or other parameters/features of the local andambient environment (e.g., company type, industry type, products,robotic handling unit present/not present, operating environment, flowrates, production rates, brand or type of auxiliary equipment (e.g.,filters, seals, coupled machinery)). The library of noise patterns maybe used by an expert system, such as one with machine learning capacity,to confirm a status of a machine, predict when maintenance is required(e.g., off-nominal measurement, artifacts in signal), predict a failureor an imminent failure, predict/diagnose a problem, and the like.

Based on a current noise pattern, the library may be consulted or usedto seed an expert system to predict an outcome, event, or state based onthe noise pattern. Based on the prediction, the expert system may one ormore of trigger an alert of a failure, imminent failure, or maintenanceevent, shut down equipment/component/line, initiatemaintenance/lubrication/alignment, deploy a field technician, recommenda vibration absorption/dampening device, modify a process to utilizebackup equipment/component, modify a process to preserveproducts/reactants, etc., generate/modify a maintenance schedule, or thelike.

For example, a noise pattern for a thermic heating system in apharmaceutical plant or cooking system may include local, ambient, andvibration noise. The ambient noise may be a result of, for example,various pumps to pump fuel into the system. Local noise may be a resultof a local security camera chirping with every detection of motion.Vibration noise may result from the combustion machinery used to heatthe thermal fluid. These noise sources may form a noise pattern whichmay be associated with a state of the thermic system. The noise patternand associated state may be stored in a library. An expert system usedto monitor the state of the thermic heating system may be seeded withnoise patterns and associated states from the library. As current dataare received into the expert system, it may predict a state based onhaving learned noise patterns and associated states.

In another example, a noise pattern for boiler feed water in a refinerymay include local and ambient noise. The local noise may be attributedto the operation of, for example, a feed pump feeding the feed waterinto a steam drum. The ambient noise may be attributed to nearby fans.These noise sources may form a noise pattern which may be associatedwith a state of the boiler feed water. The noise pattern and associatedstate may be stored in a library. An expert system used to monitor thestate of the boiler may be seeded with noise patterns and associatedstates from the library. As current data are received into the expertsystem, it may predict a state based on having learned noise patternsand associated states.

In yet another example, a noise pattern for a storage tank in a refinerymay include local, ambient, and vibration noise. The ambient noise maybe a result of, for example, a pump that pumps a product into the tank.Local noise may be a result of a fan ventilating the tank room.Vibration noise may result from line noise of a power supply into thestorage tank. These noise sources may form a noise pattern which may beassociated with a state of the storage tank. The noise pattern andassociated state may be stored in a library. An expert system used tomonitor the state of the storage tank may be seeded with noise patternsand associated states from the library. As current data are receivedinto the expert system, it may predict a state based on having learnednoise patterns and associated states.

In another example, a noise pattern for condensate/make-up water systemin a power station may include vibration and ambient noise. The ambientnoise may be attributed to nearby fans. The vibration noise may beattributed to the operation of the condenser. These noise sources mayform a noise pattern which may be associated with a state of thecondensate/make-up water system. The noise pattern and associated statemay be stored in a library. An expert system used to monitor the stateof the condensate/make-up water system may be seeded with noise patternsand associated states from the library. As current data are receivedinto the expert system, it may predict a state based on having learnednoise patterns and associated states.

A library of noise patterns may be updated if a changed parameterresulted in a new noise pattern or if a predicted outcome or state didnot occur in the absence of mitigation of a diagnosed problem. A libraryof noise patterns may be updated if a noise pattern resulted in analternative state than what was predicted by the library. The update mayoccur after just one time that the state that actually occurred did notmatch the predicted state from the library. In other embodiments, it mayoccur after a threshold number of times. In embodiments, the library maybe updated to apply one or more rules for comparison, such as rules thatgovern how many parameters to match along with the noise pattern, or thestandard deviation for the match in order to accept the predictedoutcome. For example, a baffle may be replaced in a static agitator in apharmaceutical processing plant which may result in a changed noisepattern. In another example, as the seal on a pressure cooker in a foodprocessing plant ages, the noise pattern associated with the pressurecooker may change.

In embodiments, the library of vibration fingerprints, noise sourcesand/or noise patterns may be available for subscription. The librariesmay be used in offset systems to improve operation of the local system.Subscribers may subscribe at any level (e.g., component, machinery,installation, etc.) in order to access data that would normally not beavailable to them, such as because it is from a competitor, or is froman installation of the machinery in a different industry not typicallyconsidered. Subscribers may search on indicators/predictors based on orfiltered by system conditions, or update an indicator/predictor withproprietary data to customize the library. The library may furtherinclude parameters and metadata auto-generated by deployed sensorsthroughout an installation, onboard diagnostic systems andinstrumentation and sensors, ambient sensors in the environment, sensors(e.g., in flexible sets) that can be put into place temporarily, such asin one or more mobile data collectors, sensors that can be put intoplace for longer term use, such as being attached to points of intereston devices or systems, and the like.

In embodiments, a third party (e.g., RMOs, manufacturers) can aggregatedata at the component level, equipment level, factory/installation leveland provide a statistically valid data set against which to optimizetheir own systems. For example, when a new installation of a machine iscontemplated, it may be beneficial to review a library for best datapoints to acquire in making state predictions. For example, a particularsensor package may be recommended to reliably determine if there will bea failure. For example, if vibration noise of equipment coupled withparticular levels of local noise or other ambient sensed conditionsreliably is an indicator of imminent failure, a given vibrationtransducer/temp/microphone package observing those elements may berecommended for the installation. Knowing such information may informthe choice to rent or buy a piece of machinery or associated warrantiesand service plans, such as based on knowing the quantity and depth ofinformation that may be needed to reliably maintain the machinery.

In embodiments, manufacturers may utilize the library to rapidly collectin-service information for machines to draft engineering specificationsfor new customers.

In embodiments, noise and vibration data may be used to remotely monitorinstalls and automatically dispatch a field crew.

In embodiments, noise and vibration data may be used to audit a system.For example, equipment running outside the range of a licensed dutycycle may be detected by a suite of vibration sensors and/orambient/local noise sensors. In embodiments, alerts may be triggered ofpotential out-of-warranty violations based on data from vibrationsensors and/or ambient/local noise sensors.

In embodiments, noise and vibration data may be used in maintenance.This may be particularly useful where multiple machines are deployedthat may vibrationally interact with the environment, such as two largegenerating machines on the same floor or platform with each other, suchas in power generation plants.

In embodiments, the monitoring system 10800 for data collection in anindustrial environment, may include a plurality of sensors 10802selected among vibration sensors, ambient environment condition sensorsand local sensors for collecting non-vibration data proximal to amachine in the environment, the plurality of sensors 10802communicatively coupled to the data collector 10804, the data collectioncircuit 10808 structured to collect output data 10810 from the pluralityof sensors 10802, and a machine learning data analysis circuit 10812structured to receive the output data 10810 and learn received outputdata patterns 10814 predictive of at least one of an outcome and astate. The state may correspond to an outcome relating to a machine inthe environment, an anticipated outcome relating to a machine in theenvironment, an outcome relating to a process in the environment, or ananticipated outcome relating to a process in the environment. The systemmay be deployed on the data collector 10804 or distributed between thedata collector 10804 and a remote infrastructure. The data collector10804 may include the data collection circuit 10808. The ambientenvironment condition or local sensors include one or more of a noisesensor, a temperature sensor, a flow sensor, a pressure sensor, achemical sensor, a vibration sensor, an acceleration sensor, anaccelerometer, a Pressure sensor, a force sensor, a position sensor, alocation sensor, a velocity sensor, a displacement sensor, a temperaturesensor, a thermographic sensor, a heat flux sensor, a tachometer sensor,a motion sensor, a magnetic field sensor, an electrical field sensor, agalvanic sensor, a current sensor, a flow sensor, a gaseous flow sensor,a non-gaseous fluid flow sensor, a heat flow sensor, a particulate flowsensor, a level sensor, a proximity sensor, a toxic gas sensor, achemical sensor, a CBRNE sensor, a pH sensor, a hygrometer, a moisturesensor, a densitometer, an imaging sensor, a camera, an SSR, a triaxprobe, an ultrasonic sensor, a touch sensor, a microphone, a capacitivesensor, a strain gauge, an EMF meter, and the like.

In embodiments, the monitoring system 10800 for data collection in anindustrial environment may include the data collection circuit 10808structured to collect the output data 10810 from the plurality ofsensors 10802 selected among vibration sensors, ambient environmentcondition sensors and local sensors for collecting non-vibration dataproximal to a machine in the environment, the plurality of sensors 10802communicatively coupled to the data collection circuit 10808, and amachine learning data analysis circuit 10812 structured to receive theoutput data 10810 and learn received output data patterns 10814predictive of at least one of an outcome and a state. In embodiments,the monitoring system 10800 is structured to determine if the outputdata matches a learned received output data pattern. The machinelearning data analysis circuit 10812 may be structured to learn receivedoutput data patterns 10814 by being seeded with a model 10816. The model10816 may be a physical model, an operational model, or a system model.The machine learning data analysis circuit 10812 may be structured tolearn received output data patterns 10814 based on the outcome or thestate. The monitoring system 10700 keeps or modifies operationalparameters or equipment based on the predicted outcome or the state. Thedata collection circuit 10808 collects more or fewer data points fromone or more of the plurality of sensors 10802 based on the learnedreceived output data patterns 10814, the outcome or the state. The datacollection circuit 10808 changes a data storage technique for the outputdata based on the learned received output data patterns 10814, theoutcome, or the state. The data collector 10804 changes a datapresentation mode or manner based on the learned received output datapatterns 10814, the outcome, or the state. The data collection circuit10808 applies one or more filters (low pass, high pass, band pass, etc.)to the output data. The data collection circuit 10808 adjusts theweights/biases of the machine learning data analysis circuit 10812, suchas in response to the learned received output data patterns 10814. Themonitoring system 10800 removes/re-tasks under-utilized equipment basedon one or more of the learned received output data patterns 10814, theoutcome, or the state. The machine learning data analysis circuit 10812may include a neural network expert system. The machine learning dataanalysis circuit 10812 may be structured to learn received output datapatterns 10814 indicative of progress/alignment with one or moregoals/guidelines. In embodiments, progress/alignment of eachgoal/guideline is determined by a different subset of the plurality ofsensors 10802. The machine learning data analysis circuit 10812 may bestructured to learn received output data patterns 10814 indicative of anunknown variable. The machine learning data analysis circuit 10812 maybe structured to learn received output data patterns 10814 indicative ofa preferred input sensor among available input sensors. The machinelearning data analysis circuit 10812 may be disposed in part on amachine, on one or more data collection circuits 10808, in networkinfrastructure, in the cloud, or any combination thereof. The outputdata 10810 from the vibration sensors forms a vibration fingerprint,which may include one or more of a frequency, a spectrum, a velocity, apeak location, a wave peak shape, a waveform shape, a wave envelopeshape, an acceleration, a phase information, and a phase shift. The datacollection circuit 10808 may apply a rule regarding how many parametersof the vibration fingerprint to match or the standard deviation for thematch in order to identify a match between the output data 10810 and thelearned received output data pattern. The state may be one of a normaloperation, a maintenance required, a failure, or an imminent failure.The monitoring system 10800 may trigger an alert, shut downequipment/component/line, initiate maintenance/lubrication/alignmentbased on the predicted outcome or state, deploy a field technician basedon the predicted outcome or state, recommend a vibrationabsorption/dampening device based on the predicted outcome or state,modify a process to utilize backup equipment/component based on thepredicted outcome or state, and the like. The monitoring system 10800may modify a process to preserve products/reactants, etc. based on thepredicted outcome or state. The monitoring system 10800 may generate ormodify a maintenance schedule based on the predicted outcome or state.The data collection circuit 10808 may include the data collectioncircuit 10808. The system may be deployed on the data collection circuit10808 or distributed between the data collection circuit 10808 and aremote infrastructure.

In embodiments, the monitoring system 10800 for data collection in anindustrial environment may include the data collection circuit 10808structured to collect the output data 10810 from the plurality ofsensors 10802 selected among vibration sensors, ambient environmentcondition sensors and local sensors for collecting non-vibration dataproximal to a machine in the environment, the plurality of sensors 10802communicatively coupled to the data collection circuit 10808, and amachine learning data analysis circuit 10812 structured to receive theoutput data 10810 and learn received output data patterns 10814predictive of at least one of an outcome and a state. In embodiments,the monitoring system 10800 is structured to determine if the outputdata matches a learned received output data pattern and keep or modifyoperational parameters or equipment based on the determination.

In embodiments, the monitoring system 10800 for data collection in anindustrial environment may include the data collection circuit 10808structured to collect the output data 10810 from the plurality ofsensors 10802 selected among vibration sensors, ambient environmentcondition sensors and local sensors for collecting non-vibration dataproximal to a machine in the environment, the plurality of sensors 10802communicatively coupled to the data collection circuit 10808, and amachine learning data analysis circuit 10812 structured to receive theoutput data 10810 and learn received output data patterns 10814predictive of at least one of an outcome and a state. In embodiments,the output data 10810 from the vibration sensors forms a vibrationfingerprint. The vibration fingerprint may include one or more of afrequency, a spectrum, a velocity, a peak location, a wave peak shape, awaveform shape, a wave envelope shape, an acceleration, a phaseinformation, and a phase shift. The data collection circuit 10808 mayapply a rule regarding how many parameters of the vibration fingerprintto match or the standard deviation for the match in order to identify amatch between the output data 10810 and the learned received output datapattern. The monitoring system 10800 may be structured to determine ifthe output data matches a learned received output data pattern and keepor modify operational parameters or equipment based on thedetermination.

In embodiments, the monitoring system 10800 for data collection in anindustrial environment may include a data collection band circuit 10818that identifies a subset of the plurality of sensors 10802 from which toprocess output data, the sensors selected among vibration sensors,ambient environment condition sensors and local sensors for collectingnon-vibration data proximal to a machine in the environment, theplurality of sensors 10802 communicatively coupled to the datacollection band circuit 10818, the data collection circuit 10808structured to collect the output data 10810 from the subset of theplurality of sensors 10802, and the machine learning data analysiscircuit 10812 structured to receive the output data 10810 and learnreceived output data patterns 10814 predictive of at least one of anoutcome and a state. In embodiments, when the learned received outputdata patterns 10814 do not reliably predict the outcome or the state,the data collection band circuit 10818 alters at least one parameter ofat least one of the plurality of sensors 10802. A controller 10806identifies a new data collection band circuit 10818 based on one or moreof the learned received output data patterns 10814 and the outcome orstate. The machine learning data analysis circuit 10812 may be furtherstructured to learn received output data patterns 10814 indicative of apreferred input data collection band among available input datacollection bands. The system may be deployed on the data collectioncircuit 10808 or distributed between the data collection circuit 10808and a remote infrastructure.

In embodiments, a monitoring system for data collection in an industrialenvironment may include the data collection circuit 10808 structured tocollect the output data 10810 from the plurality of sensors 10802, thesensors selected among vibration sensors, ambient environment conditionsensors and local sensors for collecting non-vibration data proximal toa machine in the environment, the plurality of sensors 10802communicatively coupled to the data collection circuit 10808. Inembodiments, the output data 10810 from the vibration sensors is in theform of a vibration fingerprint, a data structure 10820 comprising aplurality of vibration fingerprints and associated outcomes, and amachine learning data analysis circuit 10812 structured to receive theoutput data 10810 and learn received output data patterns 10814predictive of an outcome or a state based on processing of the vibrationfingerprints. The machine learning data analysis circuit 10812 may beseeded with one of the plurality of vibration fingerprints from the datastructure 10820. The data structure 10820 may be updated if a changedparameter resulted in a new vibration fingerprint or if a predictedoutcome did not occur in the absence of mitigation. The data structure10820 may be updated when the learned received output data patterns10814 do not reliably predict the outcome or the state. The system maybe deployed on the data collection circuit or distributed between thedata collection circuit and a remote infrastructure.

In embodiments, the monitoring system 10800 for data collection in anindustrial environment may include the data collection circuit 10808structured to collect the output data 10810 from the plurality ofsensors 10802 selected among vibration sensors, ambient environmentcondition sensors and local sensors for collecting non-vibration dataproximal to a machine in the environment, the plurality of sensors 10802communicatively coupled to the data collection circuit 10808. Inembodiments, the output data 10810 from the plurality of sensors 10802is in the form of a noise pattern, the data structure 10820 comprising aplurality of noise patterns and associated outcomes, and a machinelearning data analysis circuit 10812 structured to receive the outputdata 10810 and learn received output data patterns 10814 predictive ofan outcome or a state based on processing of the noise patterns.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a plurality of sensors selected amongvibration sensors, ambient environment condition sensors and localsensors for collecting non-vibration data proximal to a machine in theenvironment, the plurality of sensors communicatively coupled to a datacollector; a data collection circuit structured to collect output datafrom the plurality of sensors; and a machine learning data analysiscircuit structured to receive the output data and learn received outputdata patterns predictive of at least one of an outcome and a state. Thestate may correspond to an outcome, anticipated outcome, outcomerelating to a process, as relating to a machine in the environment. Thesystem may be deployed on the data collector. The system may bedistributed between the data collector and a remote infrastructure. Theambient environment condition sensors may include a noise sensor, atemperature sensor, a flow sensor, a pressure sensor, include a chemicalsensor, a noise sensor, a temperature sensor, a flow sensor, a pressuresensor, a chemical sensor, a vibration sensor, an acceleration sensor,an accelerometer, a pressure sensor, a force sensor, a position sensor,a location sensor, a velocity sensor, a displacement sensor, atemperature sensor, a thermographic sensor, a heat flux sensor, atachometer sensor, a motion sensor, a magnetic field sensor, anelectrical field sensor, a galvanic sensor, a current sensor, a flowsensor, a gaseous flow sensor, a non-gaseous fluid flow sensor, a heatflow sensor, a particulate flow sensor, a level sensor, a proximitysensor, a toxic gas sensor, a chemical sensor, a CBRNE sensor, a pHsensor, a hygrometer, a moisture sensor, a densitometer, an imagingsensor, a camera, an SSR, a triax probe, an ultrasonic sensor, a touchsensor, a microphone, a capacitive sensor, a strain gauge, and an EMFmeter. The local sensors may comprise one or more of a vibration sensor,an acceleration sensor, an accelerometer, a pressure sensor, a forcesensor, a position sensor, a location sensor, a velocity sensor, adisplacement sensor, a temperature sensor, a thermographic sensor, aheat flux sensor, a tachometer sensor, a motion sensor, a magnetic fieldsensor, an electrical field sensor, a galvanic sensor, a current sensor,a flow sensor, a gaseous flow sensor, a non-gaseous fluid flow sensor, aheat flow sensor, a particulate flow sensor, a level sensor, a proximitysensor, a toxic gas sensor, a chemical sensor, a CBRNE sensor, a pHsensor, a hygrometer, a moisture sensor, a densitometer, an imagingsensor, a camera, an SSR, a triax probe, an ultrasonic sensor, a touchsensor, a microphone, a capacitive sensor, a strain gauge, and an EMFmeter.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collection circuit structured tocollect output data from a plurality of sensors selected among vibrationsensors, ambient environment condition sensors and local sensors forcollecting non-vibration data proximal to a machine in the environment,the plurality of sensors communicatively coupled to the data collectioncircuit; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternspredictive of at least one of an outcome and a state. In embodiments,the monitoring system is structured to determine if the output datamatches a learned received output data pattern. In embodiments, themachine learning data analysis circuit may be structured to learnreceived output data patterns by being seeded with a model, such aswhere the model is a physical model, an operational model, or a systemmodel. The machine learning data analysis circuit may be structured tolearn received output data patterns based on the outcome or the state.The monitoring system may keep or modify operational parameters orequipment based on the predicted outcome or the state. The datacollection circuit collects data points from one or more of theplurality of sensors based on the learned received output data patterns,the outcome, or the state. The data collection circuit may change a datastorage technique for the output data based on the learned receivedoutput data patterns, the outcome, or the state. The data collectioncircuit may change a data presentation mode or manner based on thelearned received output data patterns, the outcome, or the state. Thedata collection circuit may apply one or more filters (low pass, highpass, band pass, etc.) to the output data. The data collection circuitmay adjust the weights/biases of the machine learning data analysiscircuit, such as where the adjustment is in response to the learnedreceived output data patterns. The monitoring system may remove, orre-task under-utilized equipment based on one or more of the learnedreceived output data patterns, the outcome, or the state. The machinelearning data analysis circuit may include a neural network expertsystem. The machine learning data analysis circuit may be structured tolearn received output data patterns indicative of progress/alignmentwith one or more goals or guidelines, such as where progress oralignment of each goal or guideline is determined by a different subsetof the plurality of sensors. The machine learning data analysis circuitmay be structured to learn received output data patterns indicative ofan unknown variable. The machine learning data analysis circuit may bestructured to learn received output data patterns indicative of apreferred input sensor among available input sensors. The machinelearning data analysis circuit may be disposed in part on a machine, onone or more data collectors, in network infrastructure, in the cloud, orany combination thereof. The output data from the vibration sensors mayform a vibration fingerprint, such as where the vibration fingerprintincludes one or more of a frequency, a spectrum, a velocity, a peaklocation, a wave peak shape, a waveform shape, a wave envelope shape, anacceleration, a phase information, and a phase shift. The datacollection circuit may apply a rule regarding how many parameters of thevibration fingerprint to match or the standard deviation for the matchin order to identify a match between the output data and the learnedreceived output data pattern. The state may be one of a normaloperation, a maintenance required, a failure, or an imminent failure.The monitoring system may trigger an alert based on the predictedoutcome or state. The monitoring system may shut down equipment,component, or line based on the predicted outcome or state. Themonitoring system may initiate maintenance, lubrication, or alignmentbased on the predicted outcome or state. The monitoring system maydeploy a field technician based on the predicted outcome or state. Themonitoring system may recommend a vibration absorption or dampeningdevice based on the predicted outcome or state. The monitoring systemmay modify a process to utilize backup equipment or a component based onthe predicted outcome or state. The monitoring system may modify aprocess to preserve products or reactants based on the predicted outcomeor state. The monitoring system may generate or modify a maintenanceschedule based on the predicted outcome or state. The system may bedistributed between the data collector and a remote infrastructure.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collection circuit structured tocollect output data from a plurality of sensors selected among vibrationsensors, ambient environment condition sensors and local sensors forcollecting non-vibration data proximal to a machine in the environment,the plurality of sensors communicatively coupled to the data collectioncircuit; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternspredictive of at least one of an outcome and a state. In embodiments,the monitoring system is structured to determine if the output datamatches a learned received output data pattern and keep or modifyoperational parameters or equipment based on the determination.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collection circuit structured tocollect output data from a plurality of sensors selected among vibrationsensors, ambient environment condition sensors and local sensors forcollecting non-vibration data proximal to a machine in the environment,the plurality of sensors communicatively coupled to the data collectioncircuit; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternspredictive of at least one of an outcome and a state. In embodiments,the output data from the vibration sensors forms a vibrationfingerprint. In embodiments, the vibration fingerprint may comprise oneor more of a frequency, a spectrum, a velocity, a peak location, a wavepeak shape, a waveform shape, a wave envelope shape, an acceleration, aphase information, and a phase shift. The data collection circuit mayapply a rule regarding how many parameters of the vibration fingerprintto match or the standard deviation for the match in order to identify amatch between the output data and the learned received output datapattern. The monitoring system may be structured to determine if theoutput data matches a learned received output data pattern and keep ormodify operational parameters or equipment based on the determination.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collection band circuit that identifiesa subset of a plurality of sensors from which to process output data,the sensors selected among vibration sensors, ambient environmentcondition sensors and local sensors for collecting non-vibration dataproximal to a machine in the environment, the plurality of sensorscommunicatively coupled to the data collection band circuit; a datacollection circuit structured to collect the output data from the subsetof plurality of sensors; and a machine learning data analysis circuitstructured to receive the output data and learn received output datapatterns predictive of at least one of an outcome and a state. Inembodiments, when the learned received output data patterns do notreliably predict the outcome or the state, the data collection bandcircuit alters at least one parameter of at least one of the pluralityof sensors. In embodiments, the controller may identify a new datacollection band circuit based on one or more of the learned receivedoutput data patterns and the outcome or state. The machine learning dataanalysis circuit may be further structured to learn received output datapatterns indicative of a preferred input data collection band amongavailable input data collection bands. The system may be distributedbetween the data collection circuit and a remote infrastructure.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise a data collection circuit structured to collectoutput data from the plurality of sensors, the sensors selected amongvibration sensors, ambient environment condition sensors and localsensors for collecting non-vibration data proximal to a machine in theenvironment and being communicatively coupled to the data collectioncircuit. In embodiments, the output data from the vibration sensors isin the form of a vibration fingerprint; a data structure comprising aplurality of vibration fingerprints and associated outcomes; and amachine learning data analysis circuit structured to receive the outputdata and learn received output data patterns predictive of an outcome ora state based on processing of the vibration fingerprints. The machinelearning data analysis circuit may be seeded with one of the pluralityof vibration fingerprints from the data structure. The data structuremaybe updated if a changed parameter resulted in a new vibrationfingerprint or if a predicted outcome did not occur in the absence ofmitigation. The data structure may be updated when the learned receivedoutput data patterns do not reliably predict the outcome or the state.The system may be distributed between the data collection circuit and aremote infrastructure.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise a data collection circuit structured to collectoutput data from the plurality of sensors selected among vibrationsensors, ambient environment condition sensors and local sensors forcollecting non-vibration data proximal to a machine in the environment,the plurality of sensors communicatively coupled to the data collectioncircuit. In embodiments, the output data from the plurality of sensorsis in the form of a noise pattern; a data structure comprising aplurality of noise patterns and associated outcomes; and a machinelearning data analysis circuit structured to receive the output data andlearn received output data patterns predictive of an outcome or a statebased on processing of the noise patterns.

An example system for data collection in an industrial environmentincludes an industrial system having a number of components, and anumber of sensors wherein each of the sensors is operatively coupled toat least one of the components. The example system further includes asensor communication circuit that interprets a number of sensor datavalues in response to a sensed parameter group, a pattern recognitioncircuit that determines a recognized pattern value in response to aleast a portion of the sensor data values, and a sensor learning circuitthat updates the sensed parameter group in response to the recognizedpattern value. The example sensor communication circuit further adjuststhe interpreting the sensor data values in response to the updatedsensed parameter group.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system includes the sensed parameter group being a fused numberof sensors, and where the recognized pattern value further includes asecondary value including a value determined in response to the fusednumber of sensors. An example system further includes the patternrecognition circuit and the sensor learning circuit iterativelyperforming the determining the recognized pattern value and the updatingthe sensed parameter group to improve a sensing performance value. Anexample system further includes the sensing performance value include adetermination of one or more of the following: a signal-to-noiseperformance for detecting a value of interest in the industrial system;a network utilization of the sensors in the industrial system; aneffective sensing resolution for a value of interest in the industrialsystem; a power consumption value for a sensing system in the industrialsystem, the sensing system including the sensors; a calculationefficiency for determining the secondary value; an accuracy and/or aprecision of the secondary value; a redundancy capacity for determiningthe secondary value; and/or a lead time value for determining thesecondary value. Example and non-limiting calculation efficiency valuesinclude one or more determinations such as: processor operations todetermine the secondary value; memory utilization for determining thesecondary value; a number of sensor inputs from the number of sensorsfor determining the secondary value; and/or supporting data long-termstorage for supporting the secondary value.

An example system includes one or more, or all, of the sensors as analogsensors and/or as remote sensors. An example system includes thesecondary value being a value such as: a virtual sensor output value; aprocess prediction value; a process state value; a component predictionvalue; a component state value; and/or a model output value having thesensor data values from the fused number of sensors as an input. Anexample system includes the fused number of sensors being one or more ofthe combinations of sensors such as: a vibration sensor and atemperature sensor; a vibration sensor and a pressure sensor; avibration sensor and an electric field sensor; a vibration sensor and aheat flux sensor; a vibration sensor and a galvanic sensor; and/or avibration sensor and a magnetic sensor.

An example sensor learning circuit further updates the sensed parametergroup by performing an operation such as: updating a sensor selection ofthe sensed parameter group; updating a sensor sampling rate of at leastone sensor from the sensed parameter group; updating a sensor resolutionof at least one sensor from the sensed parameter group; updating astorage value corresponding to at least one sensor from the sensedparameter group; updating a priority corresponding to at least onesensor from the sensed parameter group; and/or updating at least one ofa sampling rate, sampling order, sampling phase, and/or a network pathconfiguration corresponding to at least one sensor from the sensedparameter group. An example pattern recognition circuit furtherdetermines the recognized pattern value by performing an operation suchas: determining a signal effectiveness of at least one sensor of thesensed parameter group and the updated sensed parameter group relativeto a value of interest; determining a sensitivity of at least one sensorof the sensed parameter group and the updated sensed parameter grouprelative to the value of interest; determining a predictive confidenceof at least one sensor of the sensed parameter group and the updatedsensed parameter group relative to the value of interest; determining apredictive delay time of at least one sensor of the sensed parametergroup and the updated sensed parameter group relative to the value ofinterest; determining a predictive accuracy of at least one sensor ofthe sensed parameter group and the updated sensed parameter grouprelative to the value of interest; determining a predictive precision ofat least one sensor of the sensed parameter group and the updated sensedparameter group relative to the value of interest; and/or updating therecognized pattern value in response to external feedback. Example andnon-limiting values of interest include: a virtual sensor output value;a process prediction value; a process state value; a componentprediction value; a component state value; and/or a model output valuehaving the sensor data values from the fused plurality of sensors as aninput.

An example pattern recognition circuit further accesses cloud-based dataincluding a second number of sensor data values, the second number ofsensor data values corresponding to at least one offset industrialsystem. An example sensor learning circuit further accesses thecloud-based data including a second updated sensor parameter groupcorresponding to the at least one offset industrial system.

An example procedure for data collection in an industrial environmentincludes an operation to provide a number of sensors to an industrialsystem including a number of components, each of the number of sensorsoperatively coupled to at least one of the number of components, anoperation to interpret a number of sensor data values in response to asensed parameter group, the sensed parameter group including a fusednumber of sensors from the number of sensors, an operation to determinea recognized pattern value including a secondary value determined inresponse to the number of sensor data values, an operation to update thesensed parameter group in response to the recognized pattern value, andan operation to adjust the interpreting the number of sensor data valuesin response to the updated sensed parameter group.

Certain further aspects of an example procedure are described following,any one or more of which may be included in certain embodiments. Anexample procedure includes an operation to iteratively perform thedetermining the recognized pattern value and the updating the sensedparameter group to improve a sensing performance value, wheredetermining the sensing performance value includes an least oneoperation for determining a value, such as determining: asignal-to-noise performance for detecting a value of interest in theindustrial system; a network utilization of the plurality of sensors inthe industrial system; an effective sensing resolution for a value ofinterest in the industrial system; a power consumption value for asensing system in the industrial system, the sensing system includingthe plurality of sensors; a calculation efficiency for determining thesecondary value; an accuracy and/or a precision of the secondary value;a redundancy capacity for determining the secondary value; and/or a leadtime value for determining the secondary value.

An example procedure includes an operation to update the sensedparameter group comprised by performing at least one operation such as:updating a sensor selection of the sensed parameter group; updating asensor sampling rate of at least one sensor from the sensed parametergroup; updating a sensor resolution of at least one sensor from thesensed parameter group; updating a storage value corresponding to atleast one sensor from the sensed parameter group; updating a prioritycorresponding to at least one sensor from the sensed parameter group;and/or updating at least one of a sampling rate, sampling order,sampling phase, and a network path configuration corresponding to atleast one sensor from the sensed parameter group. An example procedureincludes determining the recognized pattern value by performing at leastone operation such as: determining a signal effectiveness of at leastone sensor of the sensed parameter group and the updated sensedparameter group relative to a value of interest; determining asensitivity of at least one sensor of the sensed parameter group and theupdated sensed parameter group relative to the value of interest;determining a predictive confidence of at least one sensor of the sensedparameter group and the updated sensed parameter group relative to thevalue of interest; determining a predictive delay time of at least onesensor of the sensed parameter group and the updated sensed parametergroup relative to the value of interest; determining a predictiveaccuracy of at least one sensor of the sensed parameter group and theupdated sensed parameter group relative to the value of interest;determining a predictive precision of at least one sensor of the sensedparameter group and the updated sensed parameter group relative to thevalue of interest; and/or updating the recognized pattern value inresponse to external feedback.

The term industrial system (and similar terms) as utilized herein shouldbe understood broadly. Without limitation to any other aspect ordescription of the present disclosure, an industrial system includes anylarge scale process system, mechanical system, chemical system, assemblyline, oil and gas system (including, without limitation, production,transportation, exploration, remote operations, offshore operations,and/or refining), mining system (including, without limitation,production, exploration, transportation, remote operations, and/orunderground operations), rail system (yards, trains, shipments, etc.),construction, power generation, aerospace, agriculture, food processing,and/or energy generation. Certain components may not be consideredindustrial individually, but may be considered industrially in anaggregated system—for example a single fan, motor, and/or engine may benot an industrial system, but may be a part of a larger system and/or beaccumulated with a number of other similar components to be consideredan industrial system and/or a part of an industrial system. In certainembodiments, a system may be considered an industrial system for somepurposes but not for other purposes—for example a large data server farmmay be considered an industrial system for certain sensing operations,such as temperature detection, vibration, or the like, but not anindustrial system for other sensing operations such as gas composition.Additionally, in certain embodiments, otherwise similar looking systemsmay be differentiated in determining whether such system are industrialsystems, and/or which type of industrial system. For example, one dataserver farm may not, at a given time, have process stream flow ratesthat are critical to operation, while another data server farm may haveprocess stream flow rates that are critical to operation (e.g., acoolant flow stream), and accordingly one data farm server may be anindustrial system for a data collection and/or sensing improvementprocess or system, while the other is not. Accordingly, the benefits ofthe present disclosure may be applied in a wide variety of systems, andany such systems may be considered an industrial system herein, while incertain embodiments a given system may not be considered an industrialsystem herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system, how to combineprocesses and systems from the present disclosure to enhance operationsof the contemplated system. Certain considerations for the person ofskill in the art, in determining whether a contemplated system is anindustrial system and/or whether aspects of the present disclosure canbenefit or enhance the contemplated system include, without limitation:the accessibility of portions of the system to positioning sensingdevices; the sensitivity of the system to capital costs (e.g., initialinstallation) and operating costs (e.g., optimization of processes,reduction of power usage); the transmission environment of the system(e.g., availability of broadband internet; satellite coverage; wirelesscellular access; the electro-magnetic (“EM”) environment of the system;the weather, temperature, and environmental conditions of the system;the availability of suitable locations to run wires, network lines, andthe like; the presence and/or availability of suitable locations fornetwork infrastructure, router positioning, and/or wireless repeaters);the availability of trained personnel to interact with computingdevices; the desired spatial, time, and/or frequency resolution ofsensed parameters in the system; the degree to which a system or processis well understood or modeled; the turndown ratio in system operations(e.g., high load differential to low load; high flow differential to lowflow; high temperature operation differential to low temperatureoperation); the turndown ratio in operating costs (e.g., effects ofpersonnel costs based on time (day, season, etc.); effects of powerconsumption cost variance with time, throughput, etc.); the sensitivityof the system to failure, down-time, or the like; the remoteness of thecontemplated system (e.g., transport costs, time delays, etc.); and/orqualitative scope of change in the system over the operating cycle(e.g., the system runs several distinct processes requiring a variablesensing environment with time; time cycle and nature of changes such asperiodic, event driven, lead times generally available, etc.). Whilespecific examples of industrial systems and considerations are describedherein for purposes of illustration, any system benefiting from thedisclosures herein, and any considerations understood to one of skill inthe art having the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The term sensor (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, sensor includes any deviceconfigured to provide a sensed value representative of a physical value(e.g., temperature, force, pressure) in a system, or representative of aconceptual value in a system at least having an ancillary relationshipto a physical value (e.g., work, state of charge, frequency, phase,etc.).

Example and non-limiting sensors include vibration, acceleration, noise,pressure, force, position, location, velocity, displacement,temperature, heat flux, speed, rotational speed (e.g., a tachometer),motion, accelerometers, magnetic field, electrical field, galvanic,current, flow (gas, fluid, heat, particulates, particles, etc.), level,proximity, gas composition, fluid composition, toxicity, corrosiveness,acidity, pH, humidity, hygrometer measures, moisture, density (bulk orspecific), ultrasound, imaging, analog, and/or digital sensors. The listof sensed values is a non-limiting example, and the benefits of thepresent disclosure in many applications can be realized independent ofthe sensor type, while in other applications the benefits of the presentdisclosure may be dependent upon the sensor type.

The sensor type and mechanism for detection may be any type of sensorunderstood in the art. Without limitation, an accelerometer may be anytype and scaling, for example 500 mV per g (1 g=9.8 m/s²), 100 mV, 1 Vper g, 5 V per g, 10 V per g, 10 MV per g, as well as any frequencycapability. It will be understood for accelerometers, and for all sensortypes, that the scaling and range may be competing (e.g., in a fixed-bitor low bit A/D system), and/or selection of high resolution scaling witha large range may drive up sensor and/or computing costs, which may beacceptable in certain embodiments, and may be prohibitive in otherembodiments. Example and non-limiting accelerometers includepiezo-electric devices, high resolution and sampling speed positiondetection devices (e.g., laser-based devices), and/or detection of otherparameters (strain, force, noise, etc.) that can be correlated toacceleration and/or vibration. Example and non-limiting proximity probesinclude electro-magnetic devices (e.g., Hall effect, VariableReluctance, etc.), a sleeve/oil film device, and/or determination ofother parameters than can be correlated to proximity. An examplevibration sensor includes a tri-axial probe, which may have highfrequency response (e.g., scaling of 100 MV/g). Example and non-limitingtemperature sensors include thermistors, thermocouples, and/or opticaltemperature determination.

A sensor may, additionally or alternatively, provide a processed value(e.g., a de-bounced, filtered, and/or compensated value) and/or a rawvalue, with processing downstream (e.g., in a data collector,controller, plant computer, and/or on a cloud-based data receiver). Incertain embodiments, a sensor provides a voltage, current, data file(e.g., for images), or other raw data output, and/or a sensor provides avalue representative of the intended sensed measurement (e.g., atemperature sensor may communicate a voltage or a temperature value).Additionally or alternatively, a sensor may communicate wirelessly,through a wired connection, through an optical connection, or by anyother mechanism. The described examples of sensor types and/orcommunication parameters are non-limiting examples for purposes ofillustration.

Additionally or alternatively, in certain embodiments, a sensor is adistributed physical device—for example where two separate sensingelements coordinate to provide a sensed value (e.g., a position sensingelement and a mass sensing element may coordinate to provide anacceleration value). In certain embodiments, a single physical devicemay form two or more sensors, and/or parts of more than one sensor. Forexample, a position sensing element may form a position sensor and avelocity sensor, where the same physical hardware provides the senseddata for both determinations.

The term smart sensor, smart device (and similar terms) as utilizedherein should be understood broadly. Without limitation to any otheraspect or description of the present disclosure, a smart sensor includesany sensor and aspect thereof as described throughout the presentdisclosure. A smart sensor includes an increment of processing reflectedin the sensed value communicated by the sensor, including at least basicsensor processing (e.g., de-bouncing, filtering, compensation,normalization, and/or output limiting), more complex compensations(e.g., correcting a temperature value based on known effects of currentenvironmental conditions on the sensed temperature value, common mode orother noise removal, etc.), a sensing device that provides the sensedvalue as a network communication, and/or a sensing device thataggregates a number of sensed values for communication (e.g., multiplesensors on a device communicated out in a parseable or deconvolutablemanner or as separate messages; multiple sensors providing a value to asingle smart sensor, which relays sensed values on to a data collector,controller, plant computer, and/or cloud-based data receiver). The useof the term smart sensor is for purposes of illustration, and whether asensor is a smart sensor can depend upon the context and thecontemplated system, and can be a relative description compared to othersensors in the contemplated system. Thus, a given sensor havingidentical functionality may be a smart sensor for the purposes of onecontemplated system, and just a sensor for the purposes of anothercontemplated system, and/or may be a smart sensor in a contemplatedsystem during certain operating conditions, and just a sensor for thepurposes of the same contemplated system during other operatingconditions.

The terms sensor fusion, fused sensors, and similar terms, as utilizedherein, should be understood broadly, except where context indicatesotherwise, without limitation to any other aspect or description of thepresent disclosure. A sensor fusion includes a determination of secondorder data from sensor data, and further includes a determination ofsecond order data from sensor data of multiple sensors, includinginvolving multiplexing of streams of data, combinations of batches ofdata, and the like from the multiple sensors. Second order data includesa determination about a system or operating condition beyond that whichis sensed directly. For example, temperature, pressure, mixing rate, andother data may be analyzed to determine which parameters areresult-effective on a desired outcome (e.g., a reaction rate). Thesensor fusion may include sensor data from multiple sources, and/orlongitudinal data (e.g., taken over a period of time, over the course ofa process, and/or over an extent of components in a plant—for exampletracking a number of assembled parts, a virtual slug of fluid passingthrough a pipeline, or the like). The sensor fusion may be performed inreal-time (e.g., populating a number of sensor fusion determinationswith sensor data as a process progresses), off-line (e.g., performed ona controller, plant computer, and/or cloud-based computing device),and/or as a post-processing operation (e.g., utilizing historical data,data from multiple plants or processes, etc.). In certain embodiments, asensor fusion includes a machine pattern recognition operation—forexample where an outcome of a process is given to the machine and/ordetermined by the machine, and the machine pattern recognition operationdetermines result-effective parameters from the detected sensor valuespace to determine which operating conditions were likely to be thecause of the outcome and/or the off-nominal result of the outcome (e.g.,process was less effective or more effective than nominal, failed,etc.). In certain embodiments, the outcome may be a quantitative outcome(e.g., 20% more product was produced than a nominal run) or aqualitative outcome (e.g., product quality was unacceptable, component Xof the contemplated system failed during the process, component X of thecontemplated system required a maintenance or service event, etc.).

In certain embodiments, a sensor fusion operation is iterative orrecursive—for example an estimated set of result effective parameters isupdated after the sensor fusion operation, and a subsequent sensorfusion operation is performed on the same data or another data set withan updated set of the result effective parameters. In certainembodiments, subsequent sensor fusion operations include adjustments tothe sensing scheme—for example higher resolution detections (e.g., intime, space, and/or frequency domains), larger data sets (and consequentcommitment of computing and/or networking resources), changes in sensorcapability and/or settings (e.g., changing an A/D scaling, range,resolution, etc.; changing to a more capable sensor and/or more capabledata collector, etc.) are performed for subsequent sensor fusionoperations. In certain embodiments, the sensor fusion operationdemonstrates improvements to the contemplated system (e.g., productionquantity, quality, and/or purity, etc.) such that expenditure ofadditional resources to improve the sensing scheme are justified. Incertain embodiments, the sensor fusion operation provides forimprovement in the sensing scheme without incremental cost—for exampleby narrowing the number of result effective parameters and therebyfreeing up system resources to provide greater resolution, samplingrates, etc., from hardware already present in the contemplated system.In certain embodiments, iterative and/or recursive sensor fusion isperformed on the same data set, a subsequent data set, and/or ahistorical data set. For example, high resolution data may already bepresent in the system, and a first sensor fusion operation is performedwith low resolution data (e.g., sampled from the high resolution dataset), such as to allow for completion of sensor fusion processingoperations within a desired time frame, within a desired processor,memory, and/or network utilization, and/or to allow for checking a largenumber of variables as potential result effective parameters. In afurther example, a greater number of samples from the high resolutiondata set may be utilized in a subsequent sensor fusion operation inresponse to confidence that improvements are present, narrowing of thepotential result effective variables, and/or a determination that higherresolution data is required to determine the result effective parametersand/or effective values for such parameters.

The described operations and aspects for sensor fusion are non-limitingexamples, and one of skill in the art, having the benefit of thedisclosures herein and information ordinarily available about acontemplated system, can readily design a system to utilize and/orbenefit from a sensor fusion operation. Certain considerations for asystem to utilize and/or benefit from a sensor fusion operation include,without limitation: the number of components in the system; the cost ofcomponents in the system; the cost of maintenance and/or down-time forthe system; the value of improvements in the system (productionquantity, quality, yield, etc.); the presence, possibility, and/orconsequences of undesirable system outcomes (e.g., side products,thermal and/or luminary events, environmental benefits or consequences,hazards present in the system); the expense of providing a multiplicityof sensors for the system; the complexity between system inputs andsystem outputs; the availability and cost of computing resources (e.g.,processing, memory, and/or communication throughput); the size/scale ofthe contemplated system and/or the ability of such a system to generatestatistically significant data; whether offset systems exist, includingwhether data from offset systems is available and whether combining datafrom offset systems will generate a statistically improved data setrelative to the system considered alone; and/or the cost of upgrading,improving, or changing a sensing scheme for the contemplated system. Thedescribed considerations for a contemplated system that may benefit fromor utilize a sensor fusion operation are non-limiting illustrations.

Certain systems, processes, operations, and/or components are describedin the present disclosure as “offset systems” or the like. An offsetsystem is a system distinct from a contemplated system, but havingrelevance to the contemplated system. For example, a contemplatedrefinery may have an “offset refinery,” which may be a refinery operatedby a competitor, by a same entity operating the contemplated refinery,and/or a historically operated refinery that no longer exists. Theoffset refinery bears some relevant relationship to the contemplatedrefinery, such as utilizing similar reactions, process flows, productionvolumes, feed stock, effluent materials, or the like. A system which isan offset system for one purpose may not be an offset system for anotherpurpose. For example, a manufacturing process utilizing conveyor beltsand similar motors may be an offset process for a contemplatedmanufacturing process for the purpose of tracking product movement,understanding motor operations and failure modes, or the like, but maynot be an offset process for product quality if the products beingproduced have distinct quality outcome parameters. Any industrial systemcontemplated herein may have an offset system for certain purposes. Oneof skill in the art, having the benefit of the present disclosure andinformation ordinarily available for a contemplated system, can readilydetermine what is disclosed by an offset system or offset aspect of asystem.

Any one or more of the terms computer, computing device, processor,circuit, and/or server include a computer of any type, capable to accessinstructions stored in communication thereto such as upon anon-transient computer readable medium, whereupon the computer performsoperations of systems or methods described herein upon executing theinstructions. In certain embodiments, such instructions themselvescomprise a computer, computing device, processor, circuit, and/orserver. Additionally or alternatively, a computer, computing device,processor, circuit, and/or server may be a separate hardware device, oneor more computing resources distributed across hardware devices, and/ormay include such aspects as logical circuits, embedded circuits,sensors, actuators, input and/or output devices, network and/orcommunication resources, memory resources of any type, processingresources of any type, and/or hardware devices configured to beresponsive to determined conditions to functionally execute one or moreoperations of systems and methods herein.

Certain operations described herein include interpreting, receiving,and/or determining one or more values, parameters, inputs, data, orother information. Operations including interpreting, receiving, and/ordetermining any value parameter, input, data, and/or other informationinclude, without limitation: receiving data via a user input; receivingdata over a network of any type; reading a data value from a memorylocation in communication with the receiving device; utilizing a defaultvalue as a received data value; estimating, calculating, or deriving adata value based on other information available to the receiving device;and/or updating any of these in response to a later received data value.In certain embodiments, a data value may be received by a firstoperation, and later updated by a second operation, as part of thereceiving a data value. For example, when communications are down,intermittent, or interrupted, a first operation to interpret, receive,and/or determine a data value may be performed, and when communicationsare restored an updated operation to interpret, receive, and/ordetermine the data value may be performed.

Certain logical groupings of operations herein, for example methods orprocedures of the current disclosure, are provided to illustrate aspectsof the present disclosure. Operations described herein are schematicallydescribed and/or depicted, and operations may be combined, divided,reordered, added, or removed in a manner consistent with the disclosureherein. It is understood that the context of an operational descriptionmay require an ordering for one or more operations, and/or an order forone or more operations may be explicitly disclosed, but the order ofoperations should be understood broadly, where any equivalent groupingof operations to provide an equivalent outcome of operations isspecifically contemplated herein. For example, if a value is used in oneoperational step, the determining of the value may be required beforethat operational step in certain contexts (e.g., where the time delay ofdata for an operation to achieve a certain effect is important), but maynot be required before that operation step in other contexts (e.g.,where usage of the value from a previous execution cycle of theoperations would be sufficient for those purposes). Accordingly, incertain embodiments an order of operations and grouping of operations asdescribed is explicitly contemplated herein, and in certain embodimentsre-ordering, subdivision, and/or different grouping of operations isexplicitly contemplated herein.

Referencing FIG. 142, an example system 10902 for data collection in anindustrial environment includes an industrial system 10904 having anumber of components 10906, and a number of sensors 10908. Inembodiments, each of the sensors 10908 is operatively coupled to atleast one of the components 10906. The selection, distribution, type,and communicative setup of sensors depends upon the application of thesystem 10902 and/or the context.

The example system 10902 further includes a sensor communication circuit10920 (reference FIG. 143) that interprets a number of sensor datavalues 10948 in response to a sensed parameter group 10928. The sensedparameter group 10928 includes a description of which sensors 10908 aresampled at which times, including at least the selected samplingfrequency, a process stage. In embodiments, a particular sensor may beproviding a value of interest, and the like. An example system includesthe sensed parameter group 10928 being a fused number of sensors 10926,for example a set of sensors believed to encompass detection ofoperating conditions of the system that affect a desired output, such asproduction output, quality, efficiency, profitability, purity,maintenance or service predictions of components in the system, failuremode predictions, and the like. In a further embodiment, a recognizedpattern value 10930 further includes a secondary value 10932 including avalue determined in response to the fused number of sensors 10926.

In certain embodiments, sensor data values 10948 are provided to a datacollector 10910, which may be in communication with multiple sensors10908 and/or with a controller 10914. In certain embodiments, a plantcomputer 10912 is additionally or alternatively present. In the examplesystem, the controller 10914 is structured to functionally executeoperations of the sensor communication circuit 10920, a patternrecognition circuit 10922, and/or a sensor learning circuit 10924, andis depicted as a separate device for clarity of description. Aspects ofthe controller 10914 may be present on the sensors 10908, the datacontroller 10910, the plant computer 10912, and/or on a cloud computingdevice 10916. In certain embodiments, all aspects of the controller10914 may be present in another device depicted on the system 10902. Theplant computer 10912 represents local computing resources, for exampleprocessing, memory, and/or network resources, that may be present and/orin communication with the industrial system 10904. In certainembodiments, the cloud computing device 10916 represents computingresources externally available to the industrial system 10904, forexample over a private network, intra-net, through cellularcommunications, satellite communications, and/or over the internet. Incertain embodiments, the data controller 10910 may be a computingdevice, a smart sensor, a MUX box, or other data collection devicecapable to receive data from multiple sensors and to pass-through thedata and/or store data for later transmission. An example datacontroller 10910 has no storage and/or limited storage, and selectivelypasses sensor data therethrough, with a subset of the sensor data beingcommunicated at a given time due to bandwidth considerations of the datacontroller 10910, a related network, and/or imposed by environmentalconstraints. In certain embodiments, one or more sensors and/orcomputing devices in the system 10902 are portable devices—for example aplant operator walking through the industrial system may have a smartphone, which the system 10902 may selectively utilize as the datacontroller 10910, sensor 10908—for example to enhance communicationthroughput, sensor resolution, and/or as a primary method forcommunicating sensor data values 10948 to the controller 10914.

The example system 10902 further includes the pattern recognitioncircuit 10922 that determines the recognized pattern value 10930 inresponse to a least a portion of the sensor data values 10948.

The example system 10902 further includes the sensor learning circuit10924 that updates the sensed parameter group 10928 in response to therecognized pattern value 10930. The example sensor communication circuit10920 further adjusts the interpreting the sensor data values 10948 inresponse to the updated sensed parameter group 10928.

The example system 10902 further includes the pattern recognitioncircuit 10922 and the sensor learning circuit 10924 iterativelyperforming the determining the recognized pattern value 10930 and theupdating the sensed parameter group 10928 to improve a sensingperformance value 10934. For example, the pattern recognition circuit10922 may add sensors, remove sensors, and/or change sensor setting tomodify the sensed parameter group 10928 based upon sensors which appearto be effective or ineffective predictors of the recognized patternvalue 10930, and the sensor learning circuit 10924 may instruct acontinued change (e.g., while improvement is still occurring), anincreased or decreased rate of change (e.g., to converge more quickly onan improved sensed parameter group 10928), and/or instruct a randomizedchange to the sensed parameter group 10928 (e.g., to ensure that allpotentially result effective sensors are being checked, and/or to avoidconverging into a local optimal value).

Example and non-limiting options for the sensing performance value 10934include: a signal-to-noise performance for detecting a value of interestin the industrial system (e.g., a determination that the predictionsignal for the value is high relative to noise factors for one or moresensors of the sensed parameter group 10928, and/or for the sensedparameter group 10928 as a whole); a network utilization of the sensorsin the industrial system (e.g., the sensor learning circuit 10924 mayscore the sensed parameter group 10928 relatively high where it is aseffective or almost as effective as another sensed parameter group10928, but results in lower network utilization); an effective sensingresolution for a value of interest in the industrial system (e.g., thesensor learning circuit 10924 may score the sensed parameter group 10928relatively high where it provides a responsive prediction of the outputvalue to smaller changes in input values); a power consumption value fora sensing system in the industrial system, the sensing system includingthe sensors (e.g., the sensor learning circuit 10924 may score thesensed parameter group 10928 relatively high where it is as effective oralmost as effective as another sensed parameter group 10928, but resultsin lower power consumption); a calculation efficiency for determiningthe secondary value (e.g., the sensor learning circuit 10924 may scorethe sensed parameter group 10928 relatively high where it is aseffective or almost as effective as another sensed parameter group 10928in determining the secondary value 10932, but results in fewer processorcycles, lower network utilization, and/or lower memory utilizationincluding stored memory requirements as well as intermediate memoryutilization such as buffers); an accuracy and/or a precision of thesecondary value (e.g., the sensor learning circuit 10924 may score thesensed parameter group 10928 relatively high where it provides a highlyaccurate and/or highly precise determination of the secondary value10932); a redundancy capacity for determining the secondary value (e.g.,the sensor learning circuit 10924 may score the sensed parameter group10928 relatively high where it provides similar capability and/orresource utilization, but provides for additional sensing redundancy,such as being more robust to gaps in data from one or more of thesensors in the sensed parameter group 10928); and/or a lead time valuefor determining the secondary value 10932 (e.g., the sensor learningcircuit 10924 may score the sensed parameter group 10928 relatively highwhere it provides an improved or sufficient lead time in the secondaryvalue 10932 determination—for example to assist in avoidingover-temperature operation, spoiling an entire production run,determining whether a component has sufficient service life to completea production run, etc.) Example and non-limiting calculation efficiencyvalues include one or more determinations such as: processor operationsto determine the secondary value 10932; memory utilization fordetermining the secondary value 10932; a number of sensor inputs fromthe number of sensors for determining the secondary value 10932; and/orsupporting memory, such as long-term storage or buffers for supportingthe secondary value 10932.

Example systems include one or more, or all, of the sensors 10908 asanalog sensors and/or as remote sensors. An example system includes thesecondary value 10932 being a value such as: a virtual sensor outputvalue; a process prediction value (e.g., a success value for aproduction run, an overtemperature value, an overpressure value, aproduct quality value, etc.); a process state value (e.g., a stage ofthe process, a temperature at a time and location in the process); acomponent prediction value (e.g., a component failure prediction, acomponent maintenance or service prediction, a component response to anoperating change prediction); a component state value (a remainingservice life or maintenance interval for a component); and/or a modeloutput value having the sensor data values 10948 from the fused numberof sensors 10926 as an input. An example system includes the fusednumber of sensors 10926 being one or more of the combinations of sensorssuch as: a vibration sensor and a temperature sensor; a vibration sensorand a pressure sensor; a vibration sensor and an electric field sensor;a vibration sensor and a heat flux sensor; a vibration sensor and agalvanic sensor; and/or a vibration sensor and a magnetic sensor.

An example sensor learning circuit 10924 further updates the sensedparameter group 10928 by performing an operation such as: updating asensor selection of the sensed parameter group 10928 (e.g., whichsensors are sampled); updating a sensor sampling rate of at least onesensor from the sensed parameter group (e.g., how fast the sensorsprovide information, and/or how fast information is passed through thenetwork); updating a sensor resolution of at least one sensor from thesensed parameter group (e.g., changing or requesting a change in asensor resolution, utilizing additional sensors to provide greatereffective resolution); updating a storage value corresponding to atleast one sensor from the sensed parameter group (e.g., storing datafrom the sensor at a higher or lower resolution, and/or over a longer orshorter time period); updating a priority corresponding to at least onesensor from the sensed parameter group (e.g., moving a sensor up to ahigher priority—for example, if environmental conditions prevent datareceipt from all planned sensors, and/or reducing a time lag betweencreation of the sensed data and receipt at the sensor learning circuit10924); and/or updating at least one of a sampling rate, sampling order,sampling phase, and/or a network path configuration corresponding to atleast one sensor from the sensed parameter group.

An example pattern recognition circuit 10922 further determines therecognized pattern value 10930 by performing an operation such as:determining a signal effectiveness of at least one sensor of the sensedparameter group and the updated sensed parameter group relative to avalue of interest 10950 (e.g., determining that a sensor value is a goodpredictor of the value of interest 10950); determining a sensitivity ofat least one sensor of the sensed parameter group 10928 and the updatedsensed parameter group 10928 relative to the value of interest 10950(e.g., determining the relative sensitivity of the determined value ofinterest to small changes in operating conditions based on the selectedsensed parameter group 10928); determining a predictive confidence of atleast one sensor of the sensed parameter group 10928 and the updatedsensed parameter group 10928 relative to the value of interest 10950;determining a predictive delay time of at least one sensor of the sensedparameter group 10928 and the updated sensed parameter group 10928relative to the value of interest 10950; determining a predictiveaccuracy of at least one sensor of the sensed parameter group 10928 andthe updated sensed parameter group 10928 relative to the value ofinterest 10950; determining a classification precision of at least onesensor of the sensed parameter group 10928 (e.g., determining theaccuracy of classification of a pattern by a machine classifier based onuse of the at least one sensor); determining a predictive precision ofat least one sensor of the sensed parameter group 10928 and the updatedsensed parameter group 10928 relative to the value of interest 10950;and/or updating the recognized pattern value 10930 in response toexternal feedback, which may be received as external data 10952 (e.g.,where an outcome is known, such as a maintenance event, product qualitydetermination, production outcome determination, etc., the detection ofthe recognized pattern value 10930 is thereby improved according to theconditions of the system before the known outcome occurred). Example andnon-limiting the values of interest 10950 include: a virtual sensoroutput value; a process prediction value; a process state value; acomponent prediction value; a component state value; and/or a modeloutput value having the sensor data values from the fused plurality ofsensors as an input.

An example pattern recognition circuit 10922 further accessescloud-based data 10954 including a second number of sensor data values,the second number of sensor data values corresponding to at least oneoffset industrial system. An example sensor learning circuit 10924further accesses the cloud-based data 10954 including a second updatedsensor parameter group corresponding to the at least one offsetindustrial system. Accordingly, the pattern recognition circuit 10922can improve pattern recognition in the system based on increasedstatistical data available from an offset system. Additionally, oralternatively, the sensor learning circuit 10924 can improve morerapidly and with greater confidence based upon the data from the offsetsystem—including determining which sensors in the offset system found tobe effective in predicting system outcomes.

An example system includes an industrial system including an oilrefinery. An example oil refinery includes one or more compressors fortransferring fluids throughout the plant, and/or for pressurizing fluidstreams (e.g., for reflux in a distillation column). Additionally, oralternatively, the example oil refinery includes vacuum distillation,for example, to fractionate hydrocarbons. The example oil refineryadditionally includes various pipelines in the system for transferringfluids, bringing in feedstock, final product delivery, and the like. Anexample system includes a number of sensors configured to determine eachaspect of a distillation column—for example temperatures of variousfluid streams, temperatures, and compositions of individual contacttrays in the column, measurements of the feed and reflux, as well as ofthe effluent or separated products. The design of a distillation columnis complex, and optimal design can depend upon the sizing of boilers,compressors, the contact conditions within the column, as well as thecomposition of feedstock, all of which can vary significantly.Additionally, the optimal position for effective sensing of conditionsin a pipeline can vary with fluid flow rates, environmental conditions(e.g., causing variation in heat transfer rates), the feedstockutilized, and other factors. Additionally, wear or loss of capability ina boiler, compressor, or other operating equipment can change the systemresponse and capabilities, rendering a single pointoptimization—including where sensors should be positioned and how theyshould sample data—to be non-optimal as the system ages.

Provision of multiple sensors throughout the system can be costly, notnecessarily because the sensors are expensive, but because the sensorsprovide data which may be prohibitive to transmit, store, and utilize.Cost may involve costs of transmitting over networks, as well as costsof operations, such as numbers of input/output operations (and timerequired to undertake such operations). The example system includesproviding a large number of sensors throughout the system, anddetermining which of the sensors are effective for control andoptimization of the distillation process. Additionally, as the feedstockand/or environmental conditions change, the optimal sensor package forboth optimization and control may change. The example system utilizes apattern recognition circuit to determine which sensors, including sensorfusion operations (including selection of groups, selection ofmultiplexing and combination, and the like), are effective incontrolling the desired parameters of the distillation, and indetermining the optimal values for temperatures, flow rates, entry traysfor feed and reflux, and/or reflux rates. Additionally, the sensorlearning circuit is capable, over time and/or utilizing offset oilrefineries, to rapidly converge on various sensor packages that areappropriate for a multiplicity of operating conditions. If an unexpectedoperating condition occurs—for example an off-nominal operation of acompressor, the sensor learning circuit is capable of migrating thesystem to the correct sensing and operating conditions for theunexpected operating condition. The ability to flexibly utilize amultiplicity of sensors allows for the system to be flexible in responseto changing conditions without providing for excessive capability intransmission and storage of sensor data. Accordingly, operations of thedistillation column are improved and can be optimized for a large numberof operating conditions. Additionally, alerts for the distillationcolumn, based upon recognition of patterns indicating off-nominaloperation, can be readily prepared to adjust or shut down the processbefore significant product quality loss and/or hazardous conditionsdevelop. Example sensor fusion operations for a refinery includevibration information combined with temperatures, pressures, and/orcomposition (e.g., to determine compressor performance); temperature andpressure, temperature and composition, and/or composition and pressure(e.g., to determine feedstock variance, contact tray performance, and/ora component failure).

An example refinery system includes storage tanks and/or boiler feedwater. Example system determinations include a sensor fusion todetermine a storage tank failure and/or off-nominal operation, such asthrough a temperature and pressure fusion, and/or a vibrationdetermination with a non-vibration determination (e.g., detecting leaks,air in the system, and/or a feed pump issue). Certain further examplesystem determinations include a sensor fusion to determine a boiler feedwater failure, such as through a sensor fusion including flow rate,pressure, temperature, and/or vibration. Any one or more of theseparameters can be utilized to determine a system leak, failure, wear ofa feed pump, scaling, and/or to reduce pumping losses while maintainingsystem flow rates. Similarly, an example industrial system includes apower generation system having a condensate and/or make-up water system,where a sensor fusion provides for a sensed parameter group andprediction of failures, maintenance, and the like.

An example industrial system includes an irrigation system for a fieldor a system of fields. Irrigations systems are subject to significantvariability in the system (e.g., inlet pressures and/or water levels,component wear and maintenance) as well as environmental variability(e.g., types and distribution of crops planted, weather, soil moisture,humidity, seasonal variability in the sun, cloud coverage, and/or windvariance). Additionally, irrigation systems tend to be remotely locatedwhere high bandwidth network access, maintenance facilities, and/or evenpersonnel for oversight are not readily available. An example systemincludes a multiplicity of sensors capable of detecting conditions forthe irrigation system, without requiring that all of the sensorstransmit or store data on a continuous basis. The pattern recognitioncircuit can readily determine the most important set of sensors toeffectively predict patterns and those system conditions requiring aresponse (e.g., irrigation cycles, positioning, and the like). Thesensor learning circuit provides for responsive migration of the sensedparameter group to variability, which may occur on slower (e.g.,seasonal, climate change, etc.) or faster cycles (e.g., equipmentfailure, weather conditions, step change events such as planting orharvesting). Additionally, alerts for remote facilities can be readilyprepared with confidence that the correct sensor package is in place fordetermining an off-nominal condition (e.g., imminent failure ormaintenance requirement for a pump).

An example industrial system includes a chemical or pharmaceuticalplant. Chemical plants require specific operating conditions, flowrates, temperatures, and the like to maintain proper temperatures,concentrations, mixing, and the like throughout the system. In manysystems, there are numerous process steps, and an off-nominal oruncoordinated operation in one part of the process can result in reducedyields, a failed process, and/or a significant reduction in productioncapacity as coordinated processes must respond (or as coordinatedprocesses fail to respond). Accordingly, a very large number of systemsare required to minimally define the system, and in certain embodimentsa prohibitive number of sensors are required, from a data transmissionand storage viewpoint, to keep sensing capability for a broad range ofoperating conditions. Additionally, the complexity of the system resultsin difficulty optimizing and coordinating system operations even wheresufficient sensors are present. In certain embodiments, the patternrecognition circuit can determine the sensing parameter groups thatprovide high resolution understanding of the system, without requiringthat all of the sensors store and transmit data continuously. Further,the utilization of a sensor fusion provides for the opportunity toabstract desired outputs, for example “maximize yield” or “minimize anundesirable side reaction” without requiring a full understanding fromthe operator of which sensors and system conditions are most effectiveto achieve the abstracted desired output. Example components in achemical or pharmaceutical plan amenable to control and predictionsbased on a sensor fusion operation include an agitator, a pressurereactor, a catalytic reactor, and/or a thermic heating system. Examplesensor fusion operations to determine sensed parameter groups and tunethe pattern recognition circuit include, without limitation, a vibrationsensor combined with another sensor type, a composition sensor combinedwith another sensor type, a flow rate determination combined withanother sensor type, and/or a temperature sensor combined with anothersensor type. The sensor fusion best suited for a particular applicationcan be converged upon by the sensor learning circuit, but also dependsupon the type of component that is subject to predictions, as well asthe type of desired outputs pursued by the operator. For example,agitators are amenable to vibration sensing, as well as uniformity ofcomposition detection (e.g., high resolution temperature), expectedreaction rates in a properly mixed system, and the like. Catalyticreactors are amenable to temperature sensing (based on the reactionthermodynamics), composition detection (e.g., for expected reactants, aswell as direct detection of catalytic material), flow rates (e.g., grossmechanical failure, reduced volume of beads, etc.), and/or pressuredetection (e.g., indicative of or coupled with flow rate changes).

An example industrial system includes a food processing system. Examplefood processing systems include pressurization vessels, stirrers,mixers, and/or thermic heating systems. Control of the process iscritical to maintain food safety, product quality, and productconsistency. However, most input parameters to the food processingsystem are subject to high variability—for example basic food productsare inherently variable as natural products, with differing watercontent, protein content, and aesthetic variation. Additionally, laborcost management, power cost management, and variability in supply water,etc., provide for a complex process where determination of the processcontrol variables, sensed parameters to determine these, andoptimization of sensing in response to process variation are a difficultproblem to resolve. Food processing systems are often cost conscious,and capital costs (e.g., for a robust network and computing system foroptimization) are not readily incurred. Further, a food processingsystem may manufacture a wide variety of products on similar or the sameproduction facilities—for example, to support an entire product lineand/or due to seasonal variations. Accordingly, a sensor setup for oneprocess may not support another process well. An example system includesthe pattern recognition circuit determining the sensing parameter groupsthat provide a strong signal response in target outcomes even in lightof high variability in system conditions. The pattern recognitioncircuit can provide for numerous sensed group parameter optionsavailable for different process conditions without requiring extensivecomputing or data storage resources. Additionally, the sensor learningcircuit provides for rapid response of the sensing system to changes inthe process conditions, including updating the sensed group parameteroptions to pursue abstracted target outputs without the operator havingto understand which sensed parameters best support the output goals. Thesensor fusion best suited for a particular application can be convergedupon by the sensor learning circuit, but also depends upon the type ofcomponent that is subject to predictions, as well as the type of desiredoutputs pursued by the operator. For example, control of and predictionsfor pressurization vessels, stirrers, mixers, and/or thermic heatingsystems are amenable to a sensor fusion with a temperature determinationcombined with a non-temperature determination, a vibration determinationcombined with a non-vibration determination, and/or a heat map combinedwith a rate of change in the heat map and/or a non-heat mapdetermination. An example system includes a sensor fusion with avibration determination and a non-vibration determination. Inembodiments, predictive information for a mixer and/or a stirrer isprovided. An example system includes a sensor fusion with a pressuredetermination, a temperature determination, and/or a non-pressuredetermination. In embodiments, predictive information for apressurization vessel is provided.

Referencing FIG. 144, an example procedure 10936 for data collection inan industrial environment includes an operation 10938 to provide anumber of sensors to an industrial system including a number ofcomponents, each of the number of sensors operatively coupled to atleast one of the number of components. The procedure 10936 furtherincludes an operation 10940 to interpret a number of sensor data valuesin response to a sensed parameter group, the sensed parameter groupincluding a fused number of sensors from the number of sensors, anoperation 10942 to determine a recognized pattern value including asecondary value determined in response to the number of sensor datavalues, an operation 10944 to update the sensed parameter group inresponse to the recognized pattern value, and an operation 10946 toadjust the interpreting the number of sensor data values in response tothe updated sensed parameter group.

The example procedure 10936 includes an operation to iteratively performthe determining the recognized pattern value and the updating the sensedparameter group to improve a sensing performance value (e.g., byrepeating the operations 10940 to 10944 periodically, at selectedintervals, and/or in response to a system change). The example procedure10936 includes determining the sensing performance value by determining:a signal-to-noise performance for detecting a value of interest in theindustrial system; a network utilization of the plurality of sensors inthe industrial system; an effective sensing resolution for a value ofinterest in the industrial system; a power consumption value for asensing system in the industrial system, the sensing system includingthe plurality of sensors; a calculation efficiency for determining thesecondary value; an accuracy and/or a precision of the secondary value;a redundancy capacity for determining the secondary value; and/or a leadtime value for determining the secondary value.

The example procedure 10936 includes the operation 10944 to update thesensed parameter group by performing at least one operation such as:updating a sensor selection of the sensed parameter group; updating asensor sampling rate of at least one sensor from the sensed parametergroup; updating a sensor resolution of at least one sensor from thesensed parameter group; updating a storage value corresponding to atleast one sensor from the sensed parameter group; updating a prioritycorresponding to at least one sensor from the sensed parameter group;and/or updating at least one of a sampling rate, sampling order,sampling phase, and a network path configuration corresponding to atleast one sensor from the sensed parameter group. The example procedure10936 includes the operation 10942 to determine the recognized patternvalue by performing at least one operation such as: determining a signaleffectiveness of at least one sensor of the sensed parameter group andthe updated sensed parameter group relative to a value of interest;determining a sensitivity of at least one sensor of the sensed parametergroup and the updated sensed parameter group relative to the value ofinterest; determining a predictive confidence of at least one sensor ofthe sensed parameter group and the updated sensed parameter grouprelative to the value of interest; determining a predictive delay timeof at least one sensor of the sensed parameter group and the updatedsensed parameter group relative to the value of interest; determining apredictive accuracy of at least one sensor of the sensed parameter groupand the updated sensed parameter group relative to the value ofinterest; determining a predictive precision of at least one sensor ofthe sensed parameter group and the updated sensed parameter grouprelative to the value of interest; and/or updating the recognizedpattern value in response to external feedback.

In embodiments, methods and systems disclosed herein include a systemfor data collection in an industrial environment. The system includes anindustrial system comprising a plurality of components, and a pluralityof sensors each operatively coupled to at least one of the plurality ofcomponents; a sensor communication circuit structured to interpret aplurality of sensor data values in response to a sensed parameter group;a pattern recognition circuit structured to determine a recognizedpattern value in response to a least a portion of the plurality ofsensor data values; and a sensor learning circuit structured to updatethe sensed parameter group in response to the recognized pattern value.In embodiments, the sensor communication circuit is further structuredto adjust the interpreting of the plurality of sensor data values inresponse to the updated sensed parameter group. In embodiments, thesensed parameter group comprises a fused plurality of sensors. Inembodiments, the recognized pattern value further includes a secondaryvalue comprising a value determined in response to the fused pluralityof sensors. In embodiments, the pattern recognition circuit and sensorlearning circuit are further structured to iteratively perform thedetermining the recognized pattern value and the updating the sensedparameter group to improve a sensing performance value. In embodiments,the sensing performance value comprises at least one performancedetermination selected from the performance determinations consistingof: a signal-to-noise performance for detecting a value of interest inthe industrial system; a network utilization of the plurality of sensorsin the industrial system; an effective sensing resolution for a value ofinterest in the industrial system; and a power consumption value for asensing system in the industrial system, the sensing system includingthe plurality of sensors. In embodiments, the sensing performance valuecomprises a signal-to-noise performance for detecting a value ofinterest in the industrial system. In embodiments, the sensingperformance value comprises a network utilization of the plurality ofsensors in the industrial system. In embodiments, the sensingperformance value comprises an effective sensing resolution for a valueof interest in the industrial system. In embodiments, the sensingperformance value comprises a power consumption value for a sensingsystem in the industrial system, the sensing system including theplurality of sensors. In embodiments, the sensing performance valuecomprises a calculation efficiency for determining the secondary value.In embodiments, the calculation efficiency comprises at least one of:processor operations to determine the secondary value, memoryutilization for determining the secondary value, a number of sensorinputs from the plurality of sensors for determining the secondaryvalue, and supporting data long-term storage for supporting thesecondary value. In embodiments, the sensing performance value comprisesone of an accuracy and a precision of the secondary value. Inembodiments, the sensing performance value comprises a redundancycapacity for determining the secondary value. In embodiments, thesensing performance value comprises a lead time value for determiningthe secondary value. In embodiments, the secondary value comprises acomponent overtemperature value. In embodiments, the secondary valuecomprises one of a component maintenance time, a component failure time,and a component service life. In embodiments, the secondary valuecomprises an off nominal operating condition affecting a product qualityproduced by an operation of the industrial system. In embodiments, theplurality of sensors comprises at least one analog sensor. Inembodiments, at least one of the sensors comprises a remote sensor. Inembodiments, the secondary value comprises at least one value selectedfrom the values consisting of: a virtual sensor output value; a processprediction value; a process state value; a component prediction value; acomponent state value; and a model output value having the sensor datavalues from the fused plurality of sensors as an input. In embodiments,the fused plurality of sensors further comprises at least one pairing ofsensor types selected from the pairings consisting of: a vibrationsensor and a temperature sensor; a vibration sensor and a pressuresensor; a vibration sensor and an electric field sensor; a vibrationsensor and a heat flux sensor; a vibration sensor and a galvanic sensor;and a vibration sensor and a magnetic sensor. In embodiments, the sensorlearning circuit is further structured to update the sensed parametergroup by performing at least one operation selected from the operationsconsisting of: updating a sensor selection of the sensed parametergroup; updating a sensor sampling rate of at least one sensor from thesensed parameter group; updating a sensor resolution of at least onesensor from the sensed parameter group; updating a storage valuecorresponding to at least one sensor from the sensed parameter group;updating a priority corresponding to at least one sensor from the sensedparameter group; and updating at least one of a sampling rate, samplingorder, sampling phase, and a network path configuration corresponding toat least one sensor from the sensed parameter group.

In embodiments, the pattern recognition circuit is further structured todetermine the recognized pattern value by performing at least oneoperation selected from the operations consisting of: determining asignal effectiveness of at least one sensor of the sensed parametergroup and the updated sensed parameter group relative to a value ofinterest; determining a sensitivity of at least one sensor of the sensedparameter group and the updated sensed parameter group relative to thevalue of interest; determining a predictive confidence of at least onesensor of the sensed parameter group and the updated sensed parametergroup relative to the value of interest; determining a predictive delaytime of at least one sensor of the sensed parameter group and theupdated sensed parameter group relative to the value of interest;determining a predictive accuracy of at least one sensor of the sensedparameter group and the updated sensed parameter group relative to thevalue of interest; determining a predictive precision of at least onesensor of the sensed parameter group and the updated sensed parametergroup relative to the value of interest; and updating the recognizedpattern value in response to external feedback. In embodiments, thevalue of interest comprises at least one value selected from the valuesconsisting of: a virtual sensor output value; a process predictionvalue; a process state value; a component prediction value; a componentstate value; and a model output value having the sensor data values fromthe fused plurality of sensors as an input. In embodiments, the patternrecognition circuit is further structured to access cloud-based datacomprising a second plurality of sensor data values, the secondplurality of sensor data values corresponding to at least one offsetindustrial system. In embodiments, the sensor learning circuit isfurther structured to access the cloud-based data comprising a secondupdated sensor parameter group corresponding to the at least one offsetindustrial system.

In embodiments, methods and systems include providing a plurality ofsensors to an industrial system comprising a plurality of components,each of the plurality of sensors operatively coupled to at least one ofthe plurality of components; interpreting a plurality of sensor datavalues in response to a sensed parameter group, the sensed parametergroup comprising a fused plurality of sensors from the plurality ofsensors. The method includes determining a recognized pattern valuecomprising a secondary value determined in response to the plurality ofsensor data values; updating the sensed parameter group in response tothe recognized pattern value; and adjusting the interpreting theplurality of sensor data values in response to the updated sensedparameter group. In embodiments, the method includes iterativelyperforming the determining the recognized pattern value and the updatingthe sensed parameter group to improve a sensing performance value. Inembodiments, the method includes determining the sensing performancevalue in response to determining at least one of: a signal-to-noiseperformance for detecting a value of interest in the industrial system;a network utilization of the plurality of sensors in the industrialsystem; an effective sensing resolution for a value of interest in theindustrial system; a power consumption value for a sensing system in theindustrial system, the sensing system including the plurality ofsensors; a calculation efficiency for determining the secondary value.In embodiments, the calculation efficiency comprises at least one of:processor operations to determine the secondary value, memoryutilization for determining the secondary value, a number of sensorinputs from the plurality of sensors for determining the secondaryvalue, and supporting data long-term storage for supporting thesecondary value; one of an accuracy and a precision of the secondaryvalue; a redundancy capacity for determining the secondary value; and alead time value for determining the secondary value. In embodiments,updating the sensed parameter group comprises performing at least oneoperation selected from the operations consisting of: updating a sensorselection of the sensed parameter group; updating a sensor sampling rateof at least one sensor from the sensed parameter group; updating asensor resolution of at least one sensor from the sensed parametergroup; updating a storage value corresponding to at least one sensorfrom the sensed parameter group; updating a priority corresponding to atleast one sensor from the sensed parameter group; and updating at leastone of a sampling rate, sampling order, sampling phase, and a networkpath configuration corresponding to at least one sensor from the sensedparameter group. In embodiments, determining the recognized patternvalue comprises performing at least one operation selected from theoperations consisting of: determining a signal effectiveness of at leastone sensor of the sensed parameter group and the updated sensedparameter group relative to a value of interest; determining asensitivity of at least one sensor of the sensed parameter group and theupdated sensed parameter group relative to the value of interest;determining a predictive confidence of at least one sensor of the sensedparameter group and the updated sensed parameter group relative to thevalue of interest; determining a predictive delay time of at least onesensor of the sensed parameter group and the updated sensed parametergroup relative to the value of interest; determining a predictiveaccuracy of at least one sensor of the sensed parameter group and theupdated sensed parameter group relative to the value of interest;determining a predictive precision of at least one sensor of the sensedparameter group and the updated sensed parameter group relative to thevalue of interest; and updating the recognized pattern value in responseto external feedback.

In embodiments, systems and methods disclosed herein include a systemfor data collection in an industrial environment. The system includes anindustrial system comprising a plurality of components, and a pluralityof sensors each operatively coupled to at least one of the plurality ofcomponents; a sensor communication circuit structured to interpret aplurality of sensor data values in response to a sensed parameter group.In embodiments, the sensed parameter group comprises a fused pluralityof sensors; a means for recognizing a pattern value in response to thesensed parameter group; and a means for updating the sensed parametergroup in response to the recognized pattern value. In embodiments, thesystem includes a means for iteratively updating the sensed parametergroup. In embodiments, the system includes a means for accessing atleast one of external data and a second plurality of sensor data valuescorresponding to an offset industrial system. In embodiments, the meansfor iteratively updating the sensed parameter group is furtherresponsive to the at least one of external data and the second pluralityof sensor data values. In embodiments, the system includes a means foraccessing a second sensed parameter group corresponding to the offsetindustrial system. In embodiments, the means for iteratively updating isfurther responsive to the second sensed parameter group.

In embodiments, methods and systems disclosed herein include a systemfor data collection in an industrial environment. The system includingan industrial system comprising a plurality of components, and aplurality of sensors each operatively coupled to at least one of theplurality of components; a sensor communication circuit structured tointerpret a plurality of sensor data values in response to a sensedparameter group; a pattern recognition circuit structured to determine arecognized pattern value in response to a least a portion of theplurality of sensor data values. In embodiments, the recognized patternvalue includes a secondary value comprising a value determined inresponse to the at least a portion of the plurality of sensors; a sensorlearning circuit structured to update the sensed parameter group inresponse to the recognized pattern value. In embodiments, the sensorcommunication circuit is further structured to adjust the interpretingthe plurality of sensor data values in response to the updated sensedparameter group. In embodiments, the pattern recognition circuit and thesensor learning circuit are further structured to iteratively performthe determining the recognized pattern value and the updating the sensedparameter group to improve a sensing performance value. In embodiments,the sensing performance value comprises a signal-to-noise performancefor detecting a value of interest in the industrial system. Inembodiments, the sensed parameter group comprises a fused plurality ofsensors. In embodiments, the secondary value comprises a valuedetermined in response to the fused plurality of sensors. Inembodiments, the secondary value comprises at least one value selectedfrom the values consisting of: a virtual sensor output value; a processprediction value; a process state value; a component prediction value; acomponent state value; and a model output value having the sensor datavalues from the fused plurality of sensors as an input.

In embodiments, methods and systems disclosed herein include a systemfor data collection in an industrial environment. The system includes anindustrial system comprising a plurality of components, and a pluralityof sensors each operatively coupled to at least one of the plurality ofcomponents; a sensor communication circuit structured to interpret aplurality of sensor data values in response to a sensed parameter group;a pattern recognition circuit structured to determine a recognizedpattern value in response to a least a portion of the plurality ofsensor data values. In embodiments, the system includes the recognizedpattern value includes a secondary value comprising a value determinedin response to the at least a portion of the plurality of sensors; asensor learning circuit structured to update the sensed parameter groupin response to the recognized pattern value. In embodiments, the sensorcommunication circuit is further structured to adjust the interpretingthe plurality of sensor data values in response to the updated sensedparameter group. In embodiments, the pattern recognition circuit and thesensor learning circuit are further structured to iteratively performthe determining the recognized pattern value and the updating the sensedparameter group to improve a sensing performance value. In embodiments,the sensing performance value comprises a network utilization of theplurality of sensors in the industrial system. In embodiments, thesensed parameter group comprises a fused plurality of sensors. Inembodiments, the secondary value comprises a value determined inresponse to the fused plurality of sensors. In embodiments, thesecondary value comprises at least one value selected from the valuesconsisting of: a virtual sensor output value; a process predictionvalue; a process state value; a component prediction value; a componentstate value; and a model output value having the sensor data values fromthe fused plurality of sensors as an input.

In embodiments, methods and systems disclosed herein include a systemfor data collection in an industrial environment. The system includes anindustrial system comprising a plurality of components, and a pluralityof sensors each operatively coupled to at least one of the plurality ofcomponents; a sensor communication circuit structured to interpret aplurality of sensor data values in response to a sensed parameter group;a pattern recognition circuit structured to determine a recognizedpattern value in response to a least a portion of the plurality ofsensor data values. In embodiments, the recognized pattern valueincludes a secondary value comprising a value determined in response tothe at least a portion of the plurality of sensors; a sensor learningcircuit structured to update the sensed parameter group in response tothe recognized pattern value. In embodiments, the sensor communicationcircuit is further structured to adjust the interpreting the pluralityof sensor data values in response to the updated sensed parameter group.In embodiments, the pattern recognition circuit and the sensor learningcircuit are further structured to iteratively perform the determiningthe recognized pattern value and the updating the sensed parameter groupto improve a sensing performance value. In embodiments, the sensingperformance value comprises an effective sensing resolution for a valueof interest in the industrial system. In embodiments, the systemincludes the sensed parameter group comprises a fused plurality ofsensors. In embodiments, the secondary value comprises a valuedetermined in response to the fused plurality of sensors. Inembodiments, the secondary value comprises at least one value selectedfrom the values consisting of: a virtual sensor output value; a processprediction value; a process state value; a component prediction value; acomponent state value; and a model output value having the sensor datavalues from the fused plurality of sensors as an input.

In embodiments, methods and systems disclosed herein include a systemfor data collection in an industrial environment. The system includes anindustrial system comprising a plurality of components, and a pluralityof sensors each operatively coupled to at least one of the plurality ofcomponents; a sensor communication circuit structured to interpret aplurality of sensor data values in response to a sensed parameter group;a pattern recognition circuit structured to determine a recognizedpattern value in response to a least a portion of the plurality ofsensor data values. In embodiments, the recognized pattern valueincludes a secondary value comprising a value determined in response tothe at least a portion of the plurality of sensors; a sensor learningcircuit structured to update the sensed parameter group in response tothe recognized pattern value. In embodiments, the sensor communicationcircuit is further structured to adjust the interpreting the pluralityof sensor data values in response to the updated sensed parameter group.In embodiments, the pattern recognition circuit and the sensor learningcircuit are further structured to iteratively perform the determiningthe recognized pattern value and the updating the sensed parameter groupto improve a sensing performance value. In embodiments, the sensingperformance value comprises a power consumption value for a sensingsystem in the industrial system, the sensing system including theplurality of sensors. In embodiments, the sensed parameter groupcomprises a fused plurality of sensors. In embodiments, the secondaryvalue comprises a value determined in response to the fused plurality ofsensors. In embodiments, the secondary value comprises at least onevalue selected from the values consisting of: a virtual sensor outputvalue; a process prediction value; a process state value; a componentprediction value; a component state value; and a model output valuehaving the sensor data values from the fused plurality of sensors as aninput.

Referencing FIG. 145, an example system 11000 for data collection in anindustrial environment includes an industrial system 11002 having anumber of components 11004, and a number of sensors 11006 eachoperatively coupled to at least one of the number of components 11004.The selection, distribution, type, and communicative setup of sensorsdepends upon the application of the system 11000 and/or the context.

The example system 11000 further includes a sensor communication circuit11018 (reference FIG. 146) that interprets a number of sensor datavalues 11034 in response to a sensed parameter group 11026. The sensedparameter group 11026 includes a description of which sensors 11006 aresampled at which times, including at least the selected samplingfrequency, a process stage. In embodiments, a particular sensor may beproviding a value of interest, and the like. An example system includesthe sensed parameter group 11026 being a number of sensors provided fora sensor fusion operation. In certain embodiments, the sensed parametergroup 11026 includes a set of sensors that encompass detection ofoperating conditions of the system that predict outcomes, off-nominaloperations, maintenance intervals, maintenance health states, and/orfuture state values for any of these, for a process, a component, asensor, and/or any aspect of interest for the system 11000.

In certain embodiments, sensor data values 11034 are provided to a datacollector 11008, which may be in communication with multiple sensors11006 and/or with a controller 11012. In certain embodiments, a plantcomputer 11010 is additionally or alternatively present. In the examplesystem, the controller 11012 is structured to functionally executeoperations of the sensor communication circuit 11018, a patternrecognition circuit 11020, and/or a system characterization circuit11022, and is depicted as a separate device for clarity of description.Aspects of the controller 11012 may be present on the sensors 11006, thedata collector 11008, the plant computer 11010, and/or on a cloudcomputing device 11014. In certain embodiments, all aspects of thecontroller 11012 may be present in another device depicted on the system11000. The plant computer 11010 represents local computing resources,for example processing, memory, and/or network resources, that may bepresent and/or in communication with the industrial system 11000. Incertain embodiments, the cloud computing device 11014 representscomputing resources externally available to the industrial system 11000,for example over a private network, intra-net, through cellularcommunications, satellite communications, and/or over the internet. Incertain embodiments, the data collector 11008 may be a computing device,a smart sensor, a MUX box, or other data collection device capable toreceive data from multiple sensors and to pass-through the data and/orstore data for later transmission. An example data collector 11008 hasno storage and/or limited storage, and selectively passes sensor datatherethrough, with a subset of the sensor data being communicated at agiven time due to bandwidth considerations of the data collector 11008,a related network, and/or imposed by environmental constraints. Incertain embodiments, one or more sensors and/or computing devices in thesystem 11000 are portable devices—for example a plant operator walkingthrough the industrial system may have a smart phone, which the system11000 may selectively utilize as a data collector 11008, sensor11006—for example to enhance communication throughput, sensorresolution, and/or as a primary method for communicating sensor datavalues 11034 to the controller 11012.

The example system 11000 further includes the pattern recognitioncircuit 11020 that determines a recognized pattern value 11028 inresponse to a least a portion of the sensor data values 11034, and thesystem characterization circuit 11022 that provides a systemcharacterization value 11030 for the industrial system in response tothe recognized pattern value 11028. The system characterization value11030 includes any value determined from the pattern recognitionoperations of the pattern recognition circuit 11020, includingdetermining that a system condition of interest is present, a componentcondition of interest is present, an abstracted condition of the systemor a component is present (e.g., a product quality value; an operationcost value; a component health, wear, or maintenance value; a componentcapacity value; and/or a sensor saturation value) and/or is predicted tooccur within a time frame (e.g., calendar time, operational time, and/ora process stage) of interest. Pattern recognition operations includedetermining that operations compatible with a previously known pattern,operations similar to a previously known pattern and/or extrapolatedfrom previously known pattern information (e.g., a previously knownpattern includes a temperature response for a first component, and aknown or estimated relationship between components allows for adetermination that a temperature for a second component will exceed athreshold based upon the pattern recognition for the first componentcombined with the known or estimated relationship).

Non-limiting descriptions of a number of examples of the systemcharacterization value 11030 are described following. An example systemcharacterization value 11030 includes a predicted outcome for a processassociated with the industrial system—for example a product qualitydescription, a product quantity description, a product variabilitydescription (e.g., the expected variability of a product parameterpredicted according to the operating conditions of the system), aproduct yield description, a net present value (NPV) for a process, aprocess completion time, a process chance of completion success, and/ora product purity result. The predicted outcome may be a batch prediction(e.g., a single run, or an integer number of runs, of the process, andthe associated predicted outcome), a time based prediction (e.g., theprojected outcome of the process over the next day, the next threeweeks, until a scheduled shutdown, etc.), a production definedprediction (e.g., the projected outcome over the next 1,000 units, overthe next 47 orders, etc.), and/or a rate of change based outcome (e.g.,projected for 3 component failures per month, an emissions output peryear, etc.). An example system characterization value 11030 includes apredicted future state for a process associated with the industrialsystem—for example an operating temperature at a given future time, anenergy consumption value, a volume in a tank, an emitted noise value ata school adjacent to the industrial system, and/or a rotational speed ofa pump. The predicted future state may be time based (e.g., at 4 PM onThursday), based on a state of the process (e.g., during the thirdstage, during system shutdown, etc.), and/or based on a future state ofparticular interest (e.g., peak energy consumption, highest temperaturevalue, maximum noise value, time or process stage when a maximum numberof personnel will be within 50 feet of a sensitive area, time or processstage when an aspect of the system redundancy is at a lowest point—e.g.,for determining high risk points in a process, etc.). An example systemcharacterization value 11030 includes a predicted off-nominal operationfor the process associated with the industrial system—for example when acomponent capacity of the system will exceed nominal parameters(although, possibly, not experience a failure), when any parameter inthe system will be three standard deviations away from normaloperations, when a capacity of a component will be under-utilized, etc.An example system characterization value 11030 includes a predictionvalue for one of the number of components—for example an operatingcondition at a point in time and/or process stage. An example systemcharacterization value 11030 includes a future state value for one ofthe number of components. The predicted future state of a component maybe time based, based on a state of the process, and/or based on a futurestate of particular interest (e.g., a highest or lowest value predictedfor the component). An example system characterization value 11030includes an anticipated maintenance health state information for one ofthe number of components, including at a particular time, a processstage, a lowest value predicted until a next maintenance event, etc. Anexample system characterization value 11030 includes a predictedmaintenance interval for at least one of the number of components (e.g.,based on current usage, anticipated usage, planned process operations,etc.). An example system characterization value 11030 includes apredicted off-nominal operation for one of the number of components—forexample at a selected time, a process stage, and/or a future state ofparticular interest. An example system characterization value 11030includes a predicted fault operation for one of the plurality ofcomponents—for example at a selected time, a process stage, any faultoccurrence predicted based on current usage, anticipated usage, plannedprocess operations, and/or a future state of particular interest. Anexample system characterization value 11030 includes a predictedexceedance value for one of the number of components, where theexceedance value includes exceedance of a design specification, and/orexceedance of a selected threshold. An example system characterizationvalue 11030 includes a predicted saturation value for one of theplurality of sensors for example at a selected time, a process stage,any saturation occurrence predicted based on current usage, anticipatedusage, planned process operations, and/or a future state of particularinterest.

Any values for the prediction value 11030 may be raw values (e.g., atemperature value), derivative values (e.g., a rate of change of atemperature value), accumulated values (e.g., a time spent above one ormore temperature thresholds) including weighted accumulated values,and/or integrated values (e.g., an area over a temperature-time curve ata temperature value or temperature trajectory of interest). The providedexamples list temperature, but any prediction value 11030 may beutilized, including at least vibration, system throughput, pressure,etc. In certain embodiments, combinations of one or more predictionvalues 11030 may be utilized.

It will be appreciated in light of the disclosure that combiningprediction values 11030 can create particularly powerful combinationsfor system analysis, control, and risk management, which arespecifically contemplated herein. For example, a first prediction valuemay indicate a time or process stage for a maximum flow rate through thesystem, and a second prediction value may determine the predicted stateof one or more components of the system that is present at thatparticular time or process stage. In another example, a first predictionvalue indicates a lowest margin of the system in terms of capacity todeliver (e.g., by determining a point in the process where at least onecomponent has a lowest operating margin, and/or where a group ofcomponents have a statistically lower operating margin due to the riskinduced by a number of simultaneous low operating margins), and a secondprediction value testing a system risk (e.g., loss of inlet water, lossof power, increase in temperature, change in environmental conditionsthat reduce or increase heat transfer, or that preclude the emission ofcertain effluents), and the combined risk of separate events can beassessed on the total system risk. Additionally, the prediction valuesmay be operated with a sensitivity check (e.g., varying systemconditions within margins to determine if some failure may occur). Inembodiments, the use of the prediction value allows for the sensitivitycheck to be performed with higher resolution at high risk points in theprocess.

An example system 11000 further includes a system collaboration circuit11024 that interprets external data 11036, and where the patternrecognition circuit 11020 further determines the recognized patternvalue 11028 further in response to the external data 11036. Externaldata 11036 includes, without limitation, data provided from outside thesystem 11000 and/or outside the controller 11012. Non-limiting exampleexternal data 11036 include entries from an operator (e.g., indicating afailure, a fault, and/or a service event). An example patternrecognition circuit 11020 further iteratively improves patternrecognition operations in response to the external data 11036 (e.g.,where an outcome is known, such as a maintenance event, product qualitydetermination, production outcome determination, etc., the detection ofthe recognized pattern value 11028 is thereby improved according to theconditions of the system before the known outcome occurred). Example andnon-limiting external data 11036 include data such as: an indicatedprocess success value; an indicated process failure value; an indicatedcomponent maintenance event; an indicated component failure event; anindicated process outcome value; an indicated component wear value; anindicated process operational exceedance value; an indicated componentoperational exceedance value; an indicated fault value; and/or anindicated sensor saturation value.

An example system 11000 further includes the system collaborationcircuit 11024 that interprets cloud-based data 11032 including a secondnumber of sensor data values, the second number of sensor data valuescorresponding to at least one offset industrial system, and where thepattern recognition circuit 11020 further determines the recognizedpattern value 11028 further in response to the cloud-based data 11032.An example pattern recognition circuit 11020 further iterativelyimproves pattern recognition operations in response to the cloud-baseddata 11032. An example sensed parameter group 11026 includes a triaxialvibration sensor, a vibration sensor and a second sensor that is not avibration sensor, the second sensor being a digital sensor, and/or anumber of analog sensors.

An example system includes an industrial system including an oilrefinery. An example oil refinery includes one or more compressors fortransferring fluids throughout the plant, and/or for pressurizing fluidstreams (e.g., for reflux in a distillation column). Additionally, oralternatively, the example oil refinery includes vacuum distillation,for example to fractionate hydrocarbons. The example oil refineryadditionally includes various pipelines in the system for transferringfluids, bringing in feedstock, final product delivery, and the like. Anexample system includes a number of sensors configured to determine eachaspect of a distillation column—for example temperatures of variousfluid streams, temperatures, and compositions of individual contacttrays in the column, measurements of the feed and reflux, as well as ofthe effluent or separated products. The design of a distillation columnis complex, and optimal design can depend upon the sizing of boilers,compressors, the contact conditions within the column, as well as thecomposition of feedstock, which can vary significantly. Additionally,the optimal position for effective sensing of conditions in a pipelinecan vary with fluid flow rates, environmental conditions (e.g., causingvariation in heat transfer rates), the feedstock utilized, and otherfactors. Additionally, wear or loss of capability in a boiler,compressor, or other operating equipment can change the system responseand capabilities, rendering a single point optimization, including wheresensors should be positioned and how they should sample data, to benon-optimal as the system ages.

Provision of multiple sensors throughout the system can be costly, notnecessarily because the sensors are expensive, but because the sensorsprovide data that may be prohibitive to transmit, store, and utilize.The example system includes providing a large number of sensorsthroughout the system, and predicting the future states of components,process variables, products, and/or emissions for the system. Theexample system utilizes a pattern recognition circuit to determine notonly the future predicted state of parameters, but when the futurepredicted state of parameters will be of interest, and/or will combinewith other future predicted state of parameters to create additionalrisks or opportunities.

Additionally, the system characterization circuit and the systemcollaboration circuit can improve predictions and/or systemcharacterizations over time, and/or utilizing offset oil refineries, tomore robustly make predictions or system characterizations, which canprovide for earlier detection, longer term planning for overallenterprise optimization, and/or to allow the industrial system tooperate closer to margins. If an unexpected operating conditionoccurs—for example an off-nominal operation of a compressor, the sensorcollaboration circuit is able to migrate the system prediction andimprove the capability to detect the conditions that caused theunexpected operating condition in the system, and/or in offset systems.Additionally, alerts for the distillation column, based upon predictionsindicating off-nominal operation, marginal operation, high riskoperation, and/or upcoming maintenance or potential failures, can bereadily prepared to provide visibility to risks that otherwise may notbe apparent by simply looking at system capacities and past experiencewithout rigorous analysis.

Example sensor fusion operations for a refinery include vibrationinformation combined with temperatures, pressures, and/or composition(e.g., to determine compressor performance); temperature and pressure,temperature and composition, and/or composition, and pressure (e.g., todetermine feedstock variance, contact tray performance, and/or acomponent failure).

An example refinery system includes storage tanks and/or boiler feedwater. Example system determinations include a sensor fusion todetermine a storage tank failure and/or off-nominal operation, such asthrough a temperature and pressure fusion, and/or a vibrationdetermination with a non-vibration determination (e.g., detecting leaks,air in the system, and/or a feed pump issue). Certain further examplesystem predictions include a sensor fusion to determine a boiler feedwater failure, such as through a sensor fusion including flow rate,pressure, temperature, and/or vibration. Any one or more of theseparameters can be utilized to predict a system leak, failure, wear of afeed pump, and/or scaling.

Similarly, an example industrial system includes a power generationsystem having a condensate and/or make-up water system, where a sensorfusion provides for a sensed parameter group and prediction of failures,maintenance, and the like. The system characterization circuit,utilizing sensor fusion and/or a continuous machine learning process,can predict failures, off-nominal operations, component health, and/ormaintenance events for, without limitation, compressors, piping, storagetanks, and/or boiler feed water for an oil refinery.

An example industrial system includes an irrigation system for a fieldor a system of fields. Irrigations systems are subject to significantvariability in the system (e.g., inlet pressures and/or water levels,component wear and maintenance) as well as environmental variability(e.g., types and distribution of crops planted, weather, soil moisture,humidity, seasonal variability in the sun, cloud coverage, and/or windvariance). Additionally, irrigation systems tend to be remotely locatedwhere high bandwidth network access, maintenance facilities, and/or evenpersonnel for oversight are not readily available. An example systemincludes a multiplicity of sensors capable to enable prediction ofconditions for the irrigation system, without requiring that all of thesensors transmit or store data on a continuous basis. The patternrecognition circuit can readily determine the most important set ofsensors to effectively predict patterns and thus system conditionsrequiring a response (e.g., irrigation cycles, positioning, and thelike). Additionally, alerts for remote facilities can be readilyprepared, with confidence that the correct sensor package is in placefor predicting an off-nominal condition (e.g., imminent failure ormaintenance requirement for a pump). In certain embodiments, the systemmay determine an off-nominal process condition such as water feedavailability being below normal (e.g., based upon recognized patternconditions such as recent precipitation history, water productionhistory from the irrigation system or other systems competing for thesame water feed), structured news alerts or external data, etc., andupdate the sensed parameter group, for example to confirm the water feedavailability (e.g., a water level sensor in a relevant location), toconfirm that acceptable conditions are available that water deliverylevels can be dropped (e.g., a humidity sensor, and/or a prompt to auser), and/or to confirm that sufficient available secondary sources areavailable (e.g., an auxiliary water level sensor).

An example industrial system includes a chemical or pharmaceuticalplant. Chemical plants require specific operating conditions, flowrates, temperatures, and the like to maintain proper temperatures,concentrations, mixing, and the like throughout the system. In manysystems, there are numerous process steps, and an off-nominal oruncoordinated operation in one part of the process can result in reducedyields, a failed process, and/or a significant reduction in productioncapacity as coordinated processes must respond (or as coordinatedprocesses fail to respond). Accordingly, a very large number of systemsare required to minimally define the system, and in certain embodimentsa prohibitive number of sensors are required, from a data transmissionand storage viewpoint, to keep sensing capability for a broad range ofoperating conditions. Additionally, the complexity of the system resultsin difficulty optimizing and coordinating system operations even wheresufficient sensors are present. In certain embodiments, the patternrecognition circuit can predict the sensing parameter groups thatprovide high resolution understanding of the system, without requiringthat all of the sensors store and transmit data continuously. Further,the pattern recognition circuit can highlight the predicted system risksand capacity limitations for upcoming process operations, where therisks are buried in the complex process. Accordingly, this means it canconfidently be operated closer to margins, at a lower cost, and/ormaintenance or system upgrades can be performed before failures orcapacity limitations are experienced.

Further, the utilization of a sensor fusion provides for the opportunityto abstract desired predictions, such as “maximize quality” or “minimizeand undesirable side reaction” without requiring a full understandingfrom the operator of which sensors and system conditions are mosteffective to achieve the abstracted desired output. Further, thepredictive nature of the pattern recognition circuit allows for changesin the process to support the desired outcome to be implemented beforethe process is committed to a sub-optimal outcome. Example components ina chemical or pharmaceutical plan amenable to control and predictionsbased on operations of the pattern recognition circuit and/or a sensorfusion operation include an agitator, a pressure reactor, a catalyticreactor, and/or a thermic heating system. Example sensor fusionoperations to determine sensed parameter groups and tune the patternrecognition circuit include, without limitation, a vibration sensorcombined with another sensor type, a composition sensor combined withanother sensor type, a flow rate determination combined with anothersensor type, and/or a temperature sensor combined with another sensortype. For example, agitators are amenable to vibration sensing, as wellas uniformity of composition detection (e.g., high resolutiontemperature), expected reaction rates in a properly mixed system, andthe like. Catalytic reactors are amenable to temperature sensing (basedon the reaction thermodynamics), composition detection (e.g., forexpected reactants, as well as direct detection of catalytic material),flow rates (e.g., gross mechanical failure, reduced volume of beads,etc.), and/or pressure detection (e.g., indicative of or coupled withflow rate changes).

An example industrial system includes a food processing system. Examplefood processing systems include pressurization vessels, stirrers,mixers, and/or thermic heating systems. Control of the process iscritical to maintain food safety, product quality, and productconsistency. However, most input parameters to the food processingsystem are subject to high variability—for example basic food productsare inherently variable as natural products, with differing watercontent, protein content, and other aesthetic variation. Additionally,labor cost management, power cost management, and variability in supplywater, etc., provide for a complex process where determination of thepredictive variables, sensed parameters to determine these, andoptimization of predicting in response to process variation are adifficult problem to resolve. Food processing systems are often costconscious, and capital costs (e.g., for a robust network and computingsystem for optimization) are not readily incurred. Further, a foodprocessing system may manufacture wide variance of products on similaror the same production facilities, for example to support an entireproduct line and/or due to seasonal variations, and accordingly apredictive operation for one process may not support another processwell. Example systems include the pattern recognition circuitdetermining the sensing parameter groups that provide a strong signalresponse in target outcomes even in light of high variability in systemconditions. The pattern recognition circuit can provide for numeroussensed group parameter options available for different processconditions without requiring extensive computing or data storageresources, and accordingly achieve relevant predictions for a widevariety of operating conditions. For example, control of and predictionsfor pressurization vessels, stirrers, mixers, and/or thermic heatingsystems are amenable to operations of the pattern recognition circuit,and/or a sensor fusion with a temperature determination combined with anon-temperature determination, a vibration determination combined with anon-vibration determination, and/or a heat map combined with a rate ofchange in the heat map and/or a non-heat map determination. An examplesystem includes a pattern recognition circuit operation and/or a sensorfusion with a vibration determination and a non-vibration determination.In embodiments, predictive information for a mixer and/or a stirrer isprovided; and/or with a pressure determination, a temperaturedetermination, and/or a non-pressure determination. In embodiments,predictive information for a pressurization vessel is provided.

Referencing FIG. 147, an example procedure 11038 includes an operation11040 to provide a number of sensors to an industrial system including anumber of components, each of the number of sensors operatively coupledto at least one of the number of components, an operation 11042 tointerpret a number of sensor data values in response to a sensedparameter group, the sensed parameter group including at least onesensor of the number of sensors, an operation 11044 to determine arecognized pattern value in response to a least a portion of the numberof sensor data values, and an operation 11046 to provide a systemcharacterization value for the industrial system in response to therecognized pattern value.

An example procedure 11038 further includes the operation 11046 toprovide the system characterization value by performing an operationsuch as: determining a predicted outcome for a process associated withthe industrial system; determining a predicted future state for aprocess associated with the industrial system; determining a predictedoff-nominal operation for the process associated with the industrialsystem; determining a prediction value for one of the plurality ofcomponents; determining a future state value for one of the plurality ofcomponents; determining an anticipated maintenance health stateinformation for one of the plurality of components; determining apredicted maintenance interval for at least one of the plurality ofcomponents; determining a predicted off-nominal operation for one of theplurality of components; determining a predicted fault operation for oneof the plurality of components; determining a predicted exceedance valuefor one of the plurality of components; and/or determining a predictedsaturation value for one of the plurality of sensors.

An example procedure 11038 includes an operation 11050 to interpretexternal data and/or cloud-based data, and where the operation 11044 todetermine the recognized pattern value is further in response to theexternal data and/or the cloud-based data. An example procedure 11038includes an operation to iteratively improve pattern recognitionoperations in response to the external data and/or the cloud-based data,for example by operation 11048 to adjust the operation 11042interpreting sensor values, such as by updating the sensed parametergroup. The operation to iteratively improve pattern recognition mayfurther include repeating operations 11042 through 11048, periodically,at selected intervals, in response to a system change, and/or inresponse to a prediction value of a component, process, or the system.

In embodiments, a system for data collection in an industrialenvironment may comprise: an industrial system comprising a plurality ofcomponents, and a plurality of sensors each operatively coupled to atleast one of the plurality of components; a sensor communication circuitstructured to interpret a plurality of sensor data values in response toa sensed parameter group, the sensed parameter group comprising at leastone sensor of the plurality of sensors; a pattern recognition circuitstructured to determine a recognized pattern value in response to aleast a portion of the plurality of sensor data values; and a systemcharacterization circuit structured to provide a system characterizationvalue for the industrial system in response to the recognized patternvalue. In embodiments, a characterization value may include at least onecharacterization value selected from the characterization valuesconsisting of: a predicted outcome for a process associated with theindustrial system; a predicted future state for a process associatedwith the industrial system; and a predicted off-nominal operation forthe process associated with the industrial system. The systemcharacterization value may include at least one characterization valueselected from the characterization values consisting of: a predictionvalue for one of the plurality of components; a future state value forone of the plurality of components; an anticipated maintenance healthstate information for one of the plurality of components; and apredicted maintenance interval for at least one of the plurality ofcomponents. The system characterization value may include at least onecharacterization value selected from the characterization valuesconsisting of: a predicted off-nominal operation for one of theplurality of components; a predicted fault operation for one of theplurality of components; and a predicted exceedance value for one of theplurality of components. The system characterization value may include apredicted saturation value for one of the plurality of sensors. A systemcollaboration circuit may be included that is structured to interpretexternal data. In embodiments, the pattern recognition circuit isfurther structured to determine the recognized pattern value further inresponse to the external data. The pattern recognition circuit may befurther structured to iteratively improve pattern recognition operationsin response to the external data. The external data may include at leastone of: an indicated component maintenance event; an indicated componentfailure event; an indicated component wear value; an indicated componentoperational exceedance value; and an indicated fault value. The externaldata may include at least one of: an indicated process failure value; anindicated process success value; an indicated process outcome value; andan indicated process operational exceedance value. The external data mayinclude an indicated sensor saturation value. A system collaborationcircuit may be included that is structured to interpret cloud-based datacomprising a second plurality of sensor data values, the secondplurality of sensor data values corresponding to at least one offsetindustrial system. In embodiments, the pattern recognition circuit isfurther structured to determine the recognized pattern value further inresponse to the cloud-based data. The pattern recognition circuit may befurther structured to iteratively improve pattern recognition operationsin response to the cloud-based data. The sensed parameter group mayinclude a triaxial vibration sensor. The sensed parameter group mayinclude a vibration sensor and a second sensor that is not a vibrationsensor, such as where the second sensor comprises a digital sensor. Thesensed parameter group may include a plurality of analog sensors.

In embodiments, a method may comprise: providing a plurality of sensorsto an industrial system comprising a plurality of components, each ofthe plurality of sensors operatively coupled to at least one of theplurality of components; interpreting a plurality of sensor data valuesin response to a sensed parameter group, the sensed parameter groupcomprising at least one sensor of the plurality of sensors; determininga recognized pattern value in response to a least a portion of theplurality of sensor data values; and providing a system characterizationvalue for the industrial system in response to the recognized patternvalue. The system characterization value may be provided by performingat least one operation selected from the operations consisting of:determining a prediction value for one of the plurality of components;determining a future state value for one of the plurality of components;determining an anticipated maintenance health state information for oneof the plurality of components; and determining a predicted maintenanceinterval for at least one of the plurality of components. The systemcharacterization value may be provided by performing at least oneoperation selected from the operations consisting of: determining apredicted outcome for a process associated with the industrial system;determining a predicted future state for a process associated with theindustrial system; and determining a predicted off-nominal operation forthe process associated with the industrial system. The systemcharacterization value may be provided by performing at least oneoperation selected from the operations consisting of: determining apredicted off-nominal operation for one of the plurality of components;determining a predicted fault operation for one of the plurality ofcomponents; and determining a predicted exceedance value for one of theplurality of components. The system characterization value may beprovided by determining a predicted saturation value for one of theplurality of sensors. Determining the recognized pattern value may befurther in response to the external data. Iteratively improving patternrecognition operations may be provided in response to the external data.Interpreting the external data may include at least one operationselected from the operations consisting of: interpreting an indicatedcomponent maintenance event; interpreting an indicated component failureevent; interpreting an indicated component wear value; interpreting anindicated component operational exceedance value; and interpreting anindicated fault value. Interpreting the external data may include atleast one operation selected from the operations consisting of:interpreting an indicated process success value; interpreting anindicated process failure value; interpreting an indicated processoutcome value; and interpreting an indicated process operationalexceedance value. Interpreting the external data may includeinterpreting an indicated sensor saturation value. Interpretingcloud-based data may include a second plurality of sensor data values,the second plurality of sensor data values corresponding to at least oneoffset industrial system. In embodiments, determining the recognizedpattern value is further in response to the cloud-based data.Iteratively improving pattern recognition operations may be provided inresponse to the cloud-based data.

In embodiments, a system for data collection in an industrialenvironment may comprise: an industrial system comprising a plurality ofcomponents, and a plurality of sensors each operatively coupled to atleast one of the plurality of components; a sensor communication circuitstructured to interpret a plurality of sensor data values in response toa sensed parameter group, the sensed parameter group comprising at leastone sensor of the plurality of sensors; a means for determining arecognized pattern value in response to at least a portion of theplurality of sensor data values; and a means for providing a systemcharacterization value for the industrial system in response to therecognized pattern value. The means for providing the systemcharacterization value may comprise a means for performing at least oneoperation selected from the operations consisting of: determining apredicted outcome for a process associated with the industrial system;determining a predicted future state for a process associated with theindustrial system; and determining a predicted off-nominal operation forthe process associated with the industrial system. The means forproviding the system characterization value may include a means forperforming at least one operation selected from the operationsconsisting of: determining a prediction value for one of the pluralityof components; determining a future state value for one of the pluralityof components; determining an anticipated maintenance health stateinformation for one of the plurality of components; and determining apredicted maintenance interval for at least one of the plurality ofcomponents. The means for providing the system characterization valuemay include a means for performing at least one operation selected fromthe operations consisting of: determining a predicted off-nominaloperation for one of the plurality of components; determining apredicted fault operation for one of the plurality of components; anddetermining a predicted exceedance value for one of the plurality ofcomponents. The means for providing the system characterization valuemay include a means for determining a predicted saturation value for oneof the plurality of sensors. A system collaboration circuit may beprovided that is structured to interpret external data. In embodiments,the means for determining the recognized pattern value determines therecognized pattern value further in response to the external data. Ameans for iteratively improving pattern recognition operations may beprovided in response to the external data. The external data may includeat least one of: an indicated process success value; an indicatedprocess failure value; and an indicated process outcome value. Theexternal data may include at least one of: an indicated componentmaintenance event; an indicated component failure event; and anindicated component wear value. The external data may include at leastone of: an indicated process operational exceedance value; an indicatedcomponent operational exceedance value; and an indicated fault value.The external data may include an indicated sensor saturation value. Asystem collaboration circuit may be provided that is structured tointerpret cloud-based data comprising a second plurality of sensor datavalues, the second plurality of sensor data values corresponding to atleast one offset industrial system. In embodiments, the means fordetermining the recognized pattern value determines the recognizedpattern value further in response to the cloud-based data. A means foriteratively improving pattern recognition operations may be provided inresponse to the cloud-based data.

In embodiments, a system for data collection in an industrialenvironment may comprise: a distillation column comprising a pluralityof components, and a plurality of sensors each operatively coupled to atleast one of the plurality of components; a sensor communication circuitstructured to interpret a plurality of sensor data values in response toa sensed parameter group, the sensed parameter group comprising at leastone sensor of the plurality of sensors; a pattern recognition circuitstructured to determine a recognized pattern value in response to aleast a portion of the plurality of sensor data values; and a systemcharacterization circuit structured to provide a system characterizationvalue for the distillation column in response to the recognized patternvalue. The plurality of components may include a thermodynamic treatmentcomponent. In embodiments, the system characterization value comprisesat least one value selected from the values consisting of: determining aprediction value for the thermodynamic treatment component; determininga future state value for the thermodynamic treatment component;determining an anticipated maintenance health state information for thethermodynamic treatment component; and determining a process ratelimitation according to a capacity of the thermodynamic treatmentcomponent. The thermodynamic treatment component may include at leastone of a compressor or a boiler.

In embodiments, a system for data collection in an industrialenvironment may comprise: a chemical process system comprising aplurality of components, and a plurality of sensors each operativelycoupled to at least one of the plurality of components; a sensorcommunication circuit structured to interpret a plurality of sensor datavalues in response to a sensed parameter group, the sensed parametergroup comprising at least one sensor of the plurality of sensors; apattern recognition circuit structured to determine a recognized patternvalue in response to a least a portion of the plurality of sensor datavalues; and a system characterization circuit structured to provide asystem characterization value for the chemical process system inresponse to the recognized pattern value. The chemical process systemmay include one of a chemical plant, a pharmaceutical plant, or an oilrefinery. The system characterization value may include at least onevalue selected from the values consisting of: a separation process valuecomprising at least one of a capacity value or a purity value; a sidereaction process value comprising a side reaction rate value; and athermodynamic treatment value comprising one of a capability, acapacity, and an anticipated maintenance health for a thermodynamictreatment component.

A system for data collection in an industrial environment, the systemcomprising:

an irrigation system comprising a plurality of components including apump, and a plurality of sensors each operatively coupled to at leastone of the plurality of components; a sensor communication circuitstructured to interpret a plurality of sensor data values in response toa sensed parameter group, the sensed parameter group comprising at leastone sensor of the plurality of sensors; a pattern recognition circuitstructured to determine a recognized pattern value in response to aleast a portion of the plurality of sensor data values; and a systemcharacterization circuit structured to provide a system characterizationvalue for the irrigation system in response to the recognized patternvalue. The system characterization value may include at least one of ananticipated maintenance health value for the pump and a future statevalue for the pump. The pattern recognition circuit may determine anoff-nominal process condition in response to the at least a portion ofthe plurality of sensor data values. In embodiments, the sensorcommunication circuit is further structured to change the sensedparameter group in response to the off-nominal process condition. Theoff-nominal process condition may include an indication of below normalwater feed availability. In embodiments, the updated sensed parametergroup comprises at least one sensor selected from the sensors consistingof: a water level sensor, a humidity sensor, and an auxiliary waterlevel sensor.

As described elsewhere herein, feedback to various intelligent and/orexpert systems, control systems (including remote and local systems,autonomous systems, and the like), and the like, which may compriserule-based systems, model-based systems, artificial intelligence (AI)systems (including neural nets, self-organizing systems, and othersdescribed throughout this disclosure), and various combinations andhybrids of those (collectively referred to herein as the “expert system”except where context indicates otherwise), may include a wide range ofinformation, including measures such as utilization measures, efficiencymeasures (e.g., power, financial such as reduction of costs), measuresof success in prediction or anticipation of states (e.g., avoidance andmitigation of faults), productivity measures (e.g., workflow), yieldmeasures, profit measures, and the like, as described herein. Inembodiments feedback to the expert system may be industry-specific,domain-specific, factory-specific, machine-specific and the like.

Industry-specific feedback for the expert system may be offered by athird party, such as a repair and maintenance organization,manufacturer, one or more consortia, and the like, or may be generatedby one or more elements of the subject system itself. Industry-specificfeedback may be aggregated, such as into one or more data structures. Inembodiments, the data are aggregated at the component level, equipmentlevel, factory/installation level, and/or industry level. Users of thedata structure(s) may access data at any level (e.g., component,equipment, factory, industry, etc.) Users may search the datastructure(s) for indicators/predictors based on or filtered by systemconditions specific to their need, or update an indicator/predictor withproprietary data to customize the data structure to their industry. Inembodiments, the expert system may be seeded with industry-specificfeedback, such as in a deep learning fashion, to provide an anticipatedoutcome or state and/or to perform actions to optimize specificmachines, devices, components, processes, and the like.

In embodiments, feedback provided to the expert system may be used inone or more smart bands to predict progress towards one or more goals.The expert system may use the feedback to determine a modification,alteration, addition, change, or the like to one or more components ofthe system that provided the feedback, as described elsewhere herein.Based on the industry-specific feedback, the expert system may alter aninput, a way of treating or storing an input or output, a sensor orsensors used to provide feedback, an operating parameter, a piece ofequipment used in the system, or any other aspect of the participants inthe industrial system that gave rise to the feedback. As describedelsewhere herein, the expert system may track multiple goals, such aswith one or more smart bands. Industry-specific feedback may be used inor by the smart bands in predicting an outcome or state relating to theone or more goals, and to recommend or instruct a change that isdirected in increasing a likelihood of achieving the outcome or state.

For example, a mixer may be used in a food processing environment or ina chemical processing environment, but the feedback that is relevant inthe food processing plant (e.g., required sterilization temperatures,food viscosity, particle density (e.g., such as measured by an opticalsensor), completion of cooking (e.g., completion of reactions involvedin baking), sanitation (e.g., absence of pathogens) may be differentthan what is relevant in the chemical processing plant (e.g., impellerspeed, velocity vectors, flow rate, absence of high contaminant levels,or the like). This industry specific feedback is useful in optimizingthe operation of the mixer in its particular environment.

In another example, the expert system may use feedback from agriculturalsystems to train a model related to an irrigation system deployed in afield. In embodiments, the industry-specific feedback relates to one ormore of an amount of water used across the industry (e.g., such asmeasured by a flowmeter), a trend of water usage over a time period(e.g., such as measured by a flowmeter), a harvest amount (e.g., such asmeasured by a weight scale), an insect infestation (e.g., such asidentified and/or measured by a drone imaging), a plant death (e.g.,such as identified and/or measured by drone imaging), and the like.

In another example of a fluid flow system (e.g., fan, pump orcompressor) controlling cooling in the manufacturing industry, theexpert system may use feedback from manufacturing of componentsinvolving materials (e.g., polymers) that require cooling during themanufacturing process, such as one or more of quality of output product,strength of output product, flexibility of output product, and the like(e.g., such as measured by a suite of sensors, including densitometer,viscometer, size exclusion chromatograph, and torque meter). If thesensors indicate that the polymer is cooling too quickly during monomerconversion, the expert system may relay an instruction to one or more ofa fan, pump, or compressor in the fluid flow system to decrease anaspect of its operation in order to meet a quality goal.

In another example of a reciprocating compressor operating in a refineryperforming refinery processes (e.g., hydrotreating, hydrocracking,isomerization, reforming), the expert system may use feedback related toone or more of an amount of sulfur, nitrogen and/or aromatics downstreamof the compressor (e.g., such as measured by a near infrared (“IR”)analyzer), the cetane/octane number or smoke point of a product (e.g.,such as with an octane analyzer), the density of a product (e.g., suchas measured by a densitometer), byproduct gas amounts (e.g., such asmeasured by an electrochemical gas sensor), and the like. In thisexample, as feedback is received during isomerization of butane toisobutene by an inline near IR analyzer measuring the amount and/orquality of isobutene, the expert system may determine that theperformance of one or more components of the isomerization system,including the reciprocating compressor, should be altered in order tomeet a production goal.

In another example of a vacuum distillation unit operating in arefinery, the expert system may use feedback related to an amount of rawgasoline recovered (e.g., such as by measuring the volume or compositionof various fractions using IR), boiling point of recovered fractions(e.g., such as with a boiling point analyzer), a vapor cooling rate(e.g., such as measured by thermometer), and the like. In this example,as feedback is received during vacuum distillation to recover diesel, asthe amounts recovered indicate off-nominal rations of production, theexpert system may instruct the vacuum distillation unit to alter afeedstock source and initiate more detailed analysis of the priorfeedstock.

In yet another example of a pipeline in a refinery, the expert systemmay use feedback related to flow type (e.g., bubble, stratified, slug,annular, transition, mist) of hydrocarbon products (e.g., such asmeasured by dye tracing), flow rate, vapor velocity (such as with a flowmeter), vapor shear, and the like. In this example, as feedback isreceived during operation of the pipeline regarding the flow type andits rate, modifications may be recommended by the expert system toimprove the flow through the pipeline.

In still another example of a paddle-type or anchor-type agitator/mixerin a pharmaceutical plant, the expert system may use feedback related todegree of mixing of high-viscosity liquids, heating of medium- tolow-viscosity liquids, a density of the mixture, a growth rate of anorganism in the mixture, and the like. In this example, as feedback isreceived during operation of the agitator that a bacterial growth rateis too high (such as measured with a spectrophotometer), the expertsystem may instruct the agitator to reduce its speed to limit the amountof air being added to the mixture or growth substrate.

In a further example of a pressure reactor in a chemical processingplant, the expert system may use feedback related to a catalyticreaction rate (such as measured by a mass spectrometer), a particledensity (such as measured by a densitometer), a biological growth rate(such as measured by a spectrophotometer), and the like. In thisexample, as feedback is received during operation of the pressurereactor that the particle density and biological growth rate areoff-nominal, the expert system may instruct the pressure reactor tomodify one or more operational parameters, such as a reduction inpressure, an increase in temperature, an increase in volume of thereaction, and the like.

In another example of a gas agitator operating in a chemical processingplant, the expert system may use feedback related to effective densityof a gassed liquid, a viscosity, a gas pressure, and the like, asmeasured by appropriate sensors or equipment. In this example, asfeedback is received during operation of the gas agitator, the expertsystem may instruct the gas agitator to modify one or more operationalparameters, such as to increase or decrease a rate of agitation.

In still another example of a pump blasting liquid type agitator in achemical processing plant, the expert system may use feedback related toa viscosity of a mixture, an optical density of a growth medium, and atemperature of a solution. In this example, as feedback is receivedduring operation of the agitator, the expert system may instruct theagitator to modify one or more operational parameters, such as toincrease or decrease a rate of agitation and/or inject additional heat.

In yet another example of a turbine type agitator in a chemicalprocessing plant, the expert system may use feedback related to avibration noise, a reaction rate of the reactants, a heat transfer, or adensity of a suspension. In this example, as feedback is received duringoperation of the agitator, the expert system may instruct the agitatorto modify one or more operational parameters, such as to increase ordecrease a rate of agitation and/or inject an additional amount ofcatalyst.

In yet another example of a static agitator mixing monomers in achemical processing plant to produce a polymer, the expert system mayuse feedback related to the viscosity of the polymer, color of thepolymer, reactivity of the polymer and the like to iterate to a newsetting or parameter for the agitator, such as for example, a settingthat alters the Reynolds number, an increase in temperature, a pressureincrease, and the like.

In a further example of a catalytic reactor in a chemical processingplant, the expert system may use feedback related to a reaction rate, aproduct concentration, a product color, and the like. In this example,as feedback is received during operation of the catalytic reactor, theexpert system may instruct the reactor to modify one or more operationalparameters, such as to increase or decrease a temperature and/or injectan additional amount of catalyst.

In yet a further example of a thermic heating systems in a chemicalprocessing or food plant, the expert system may use feedback related toBTUs out of the system, a flow rate, and the like. In this example, asfeedback is received during operation of the thermic heating system, theexpert system may instruct the system to modify one or more operationalparameters, such as to change the input feedstock, to increase the flowof the feedstock, and the like.

In still a further example of using boiler feed water in a refinery, theexpert system may use feedback related to an aeration level, atemperature, and the like. In this example, as feedback is receivedrelated to the boiler feed water, the expert system may instruct thesystem to modify one or more operational parameters of a boiler, such asto increase a reduction in aeration, to increase the flow of the feedwater, and the like.

In still a further example of a storage tank in a refinery, the expertsystem may use feedback related to a temperature, a pressure, a flowrate out of the tank, and the like. In this example, as feedback isreceived related to the storage tank, the expert system may instruct thesystem to modify one or more operational parameters of, such as toincrease cooling or heating begin agitation, and the like.

In an example of a condensate/make-up water system in a power stationthat condenses steam from turbines and recirculates it back to a boilerfeeder along with make-up water, the expert system may use feedbackrelated to measuring inward air leaks, heat transfer, and make-up waterquality. In this example, as feedback is received related to thecondensate/make-up water system, the expert system may instruct thesystem to increase a purification of the make-up water, bring a vacuumpump online, and the like.

In another example of a stirrer in a food plant, the expert system mayuse feedback related to a viscosity of the food, a color of the food, atemperature of the food, and the like. In this example, as feedback isreceived, the expert system may instruct the stirrer to speed up or slowdown, depending on the predicted success in reaching a goal.

In another example of a pressure cooker in a food plant, the expertsystem may use feedback related to a viscosity of the food, a color ofthe food, a temperature of the food, and the like. In this example, asfeedback is received, the expert system may instruct the pressure cookerto continue operating, increase a temperature, or the like, depending onthe predicted success in reaching a goal.

In an embodiment, the system 11100 for data collection in an industrialenvironment may include a plurality of input sensors 11102communicatively coupled to a controller 11106, a data collection circuit11104 structured to collect output data 11108 from the input sensors11102, and a machine learning data analysis circuit 11110 structured toreceive the output data 11108 and learn received output data patterns11112 indicative of an outcome. In embodiments, the machine learningdata analysis circuit 11110 is structured to learn received output datapatterns 11112 by being seeded with a model 11114 based onindustry-specific feedback 11118. The model 11114 may be a physicalmodel, an operational model, or a system model. The industry-specificfeedback 11118 may be one or more of a utilization measure, anefficiency measure (e.g., power and/or financial), a measure of successin prediction or anticipation of states (e.g., an avoidance andmitigation of faults), a productivity measure (e.g., a workflow), ayield measure, and a profit measure. The industry-specific feedback11118 includes an amount of power generated by a machine about which theinput sensors provide information during operation of the machine. Theindustry-specific feedback 11118 includes a measure of the output of anassembly line about which the input sensors provide information. Theindustry-specific feedback 11118 includes a failure rate of units ofproduct produced by a machine about which the input sensors provideinformation. The industry-specific feedback 11118 includes a fault rateof a machine about which the input sensors provide information. Theindustry-specific feedback 11118 includes the power utilizationefficiency of a machine about which the input sensors provideinformation. In embodiments, the machine is one of a turbine, atransformer, a generator, a compressor, one that stores energy, and onethat includes power train components (e.g., the rate of extraction of amaterial by a machine about which the input sensors provide information,the rate of production of a gas by a machine about which the inputsensors provide information, the rate of production of a hydrocarbonproduct by a machine about which the input sensors provide information),and the rate of production of a chemical product by a machine aboutwhich the input sensors provide information. The machine learning dataanalysis circuit 11110 may be further structured to learn receivedoutput data patterns 11112 based on the outcome. The system 11100 maykeep or modify operational parameters or equipment. The controller 11106may adjust the weighting of the machine learning data analysis circuit11110 based on the learned received output data patterns 11112 or theoutcome, collect more/fewer data points from the input sensors based onthe learned received output data patterns 11112 or the outcome, change adata storage technique for the output data 11108 based on the learnedreceived output data patterns 11112 or the outcome, change a datapresentation mode or manner based on the learned received output datapatterns 11112 or the outcome, and apply one or more filters (low pass,high pass, band pass, etc.) to the output data 11108. In embodiments,the system 11100 may remove/re-task under-utilized equipment based onone or more of the learned received output data patterns 11112 and theoutcome. The machine learning data analysis circuit 11110 may include aneural network expert system. The input sensors may measure vibrationand noise data. The machine learning data analysis circuit 11110 may bestructured to learn received output data patterns 11112 indicative ofprogress/alignment with one or more goals/guidelines (e.g., which may bedetermined by a different subset of the input sensors). The machinelearning data analysis circuit 11110 may be structured to learn receivedoutput data patterns 11112 indicative of an unknown variable. Themachine learning data analysis circuit 11110 may be structured to learnreceived output data patterns 11112 indicative of a preferred inputamong available inputs. The machine learning data analysis circuit 11110may be structured to learn received output data patterns 11112indicative of a preferred input data collection band among availableinput data collection bands. The machine learning data analysis circuit11110 may be disposed in part on a machine, on one or more datacollectors, in network infrastructure, in the cloud, or any combinationthereof. The system 11100 may be deployed on the data collection circuit11104. The system 11100 may be distributed between the data collectioncircuit 11104 and a remote infrastructure. The data collection circuit11104 may include a data collector.

In embodiments, the system 11100 for data collection in an industrialenvironment may include the plurality of input sensors 11102communicatively coupled to the controller 11106, the data collectioncircuit 11104 structured to collect the output data 11108 from the inputsensors, and a machine learning data analysis circuit 11110 structuredto receive the output data 11108 and learn received output data patterns11112 indicative of an outcome. In embodiments, the machine learningdata analysis circuit 11110 is structured to learn received output datapatterns 11112 by being seeded with the model 11114 based on autilization measure.

In embodiments, the system 11100 for data collection in an industrialenvironment may include the plurality of input sensors 11102communicatively coupled to the controller 11106, the data collectioncircuit 11104 structured to collect the output data 11108 from the inputsensors, and a machine learning data analysis circuit 11110 structuredto receive the output data 11108 and learn received output data patterns11112 indicative of an outcome. In embodiments, the machine learningdata analysis circuit 11110 is structured to learn received output datapatterns 11112 by being seeded with the model 11114 based on anefficiency measure.

In embodiments, the system 11100 for data collection in an industrialenvironment may include the plurality of input sensors 11102communicatively coupled to the controller 11106, the data collectioncircuit 11104 structured to collect the output data 11108 from the inputsensors, and a machine learning data analysis circuit 11110 structuredto receive the output data 11108 and learn received output data patterns11112 indicative of an outcome. In embodiments, the machine learningdata analysis circuit 11110 is structured to learn received output datapatterns 11112 by being seeded with the model 11114 based on a measureof success in prediction or anticipation of states.

In embodiments, the system 11100 for data collection in an industrialenvironment may include the plurality of input sensors 11102communicatively coupled to the controller 11106, the data collectioncircuit 11104 structured to collect the output data 11108 from the inputsensors, and a machine learning data analysis circuit 11110 structuredto receive the output data 11108 and learn received output data patterns11112 indicative of an outcome. In embodiments, the machine learningdata analysis circuit 11110 is structured to learn received output datapatterns 11112 by being seeded with the model 11114 based on aproductivity measure.

In embodiments, methods and systems disclosed herein includes a systemfor data collection in an industrial environment. The system includes aplurality of input sensors communicatively coupled to a controller; adata collection circuit structured to collect output data from the inputsensors; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternsindicative of an outcome. In embodiments, the machine learning dataanalysis circuit is structured to learn received output data patterns bybeing seeded with a model based on industry-specific feedback. Inembodiments, the model is a physical model, an operational model, or asystem model. In embodiments, the industry-specific feedback is autilization measure. In embodiments, the industry-specific feedback isan efficiency measure. In embodiments, the efficiency measure is one ofpower and financial. In embodiments, the industry-specific feedback is ameasure of success in prediction or anticipation of states. Inembodiments, the measure of success is an avoidance and mitigation offaults. In embodiments, the industry-specific feedback is a productivitymeasure. In embodiments, the productivity measure is a workflow. Inembodiments, the industry-specific feedback is a yield measure. Inembodiments, the industry-specific feedback is a profit measure. Inembodiments, the machine learning data analysis circuit is furtherstructured to learn received output data patterns based on the outcome.In embodiments, the system keeps or modifies operational parameters orequipment. In embodiments, the controller adjusts the weighting of themachine learning data analysis circuit based on the learned receivedoutput data patterns or the outcome. In embodiments, the controllercollects more/fewer data points from the input sensors based on thelearned received output data patterns or the outcome. In embodiments,the controller changes a data storage technique for the output databased on the learned received output data patterns or the outcome. Inembodiments, the controller changes a data presentation mode or mannerbased on the learned received output data patterns or the outcome. Inembodiments, the controller applies one or more filters (low pass, highpass, band pass, etc.) to the output data. In embodiments, the systemremoves/re-tasks under-utilized equipment based on one or more of thelearned received output data patterns and the outcome. In embodiments,the machine learning data analysis circuit comprises a neural networkexpert system. In embodiments, the input sensors measure vibration andnoise data. In embodiments, the machine learning data analysis circuitis structured to learn received output data patterns indicative ofprogress/alignment with one or more goals/guidelines. In embodiments,progress/alignment of each goal/guideline is determined by a differentsubset of the input sensors. In embodiments, the machine learning dataanalysis circuit is structured to learn received output data patternsindicative of an unknown variable. In embodiments, the machine learningdata analysis circuit is structured to learn received output datapatterns indicative of a preferred input among available inputs. Inembodiments, the machine learning data analysis circuit is structured tolearn received output data patterns indicative of a preferred input datacollection band among available input data collection bands. Inembodiments, the machine learning data analysis circuit is disposed inpart on a machine, on one or more data collectors, in networkinfrastructure, in the cloud, or any combination thereof. Inembodiments, the system is deployed on the data collection circuit. 29.In embodiments, the system is distributed between the data collectioncircuit and a remote infrastructure. In embodiments, theindustry-specific feedback includes an amount of power generated by amachine about which the input sensors provide information duringoperation of the machine. In embodiments, the industry-specific feedbackincludes a measure of the output of an assembly line about which theinput sensors provide information. In embodiments, the industry-specificfeedback includes a failure rate of units of product produced by amachine about which the input sensors provide information. Inembodiments, the industry-specific feedback includes a fault rate of amachine about which the input sensors provide information. Inembodiments, the industry-specific feedback includes the powerutilization efficiency of a machine about which the input sensorsprovide information.

In embodiments, the machine is a turbine. In embodiments, the machine isa transformer. In embodiments, the machine is a generator. Inembodiments, the machine is a compressor. In embodiments, the machinestores energy. In embodiments, the machine includes power traincomponents. In embodiments, the industry-specific feedback includes therate of extraction of a material by a machine about which the inputsensors provide information. In embodiments, the industry-specificfeedback includes the rate of production of a gas by a machine aboutwhich the input sensors provide information. In embodiments, theindustry-specific feedback includes the rate of production of ahydrocarbon product by a machine about which the input sensors provideinformation. In embodiments, the industry-specific feedback includes therate of production of a chemical product by a machine about which theinput sensors provide information. In embodiments, the data collectioncircuit comprises a data collector.

In embodiments, methods and systems disclosed herein include a systemfor data collection in an industrial environment. The system includes aplurality of input sensors communicatively coupled to a controller; adata collection circuit structured to collect output data from the inputsensors; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternsindicative of an outcome, In embodiments, the machine learning dataanalysis circuit is structured to learn received output data patterns bybeing seeded with a model based on a utilization measure.

In embodiments, methods and system disclosed herein include a system fordata collection in an industrial environment. The system includes aplurality of input sensors communicatively coupled to a controller; adata collection circuit structured to collect output data from the inputsensors; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternsindicative of an outcome. In embodiments, the machine learning dataanalysis circuit is structured to learn received output data patterns bybeing seeded with a model based on an efficiency measure.

In embodiments, methods and systems disclosed herein include a systemfor data collection in an industrial environment. The system includes aplurality of input sensors communicatively coupled to a controller; adata collection circuit structured to collect output data from the inputsensors; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternsindicative of an outcome. In embodiments, the machine learning dataanalysis circuit is structured to learn received output data patterns bybeing seeded with a model based on a measure of success in prediction oranticipation of states.

In embodiments, methods and systems disclosed herein include a systemfor data collection in an industrial environment. The system includes aplurality of input sensors communicatively coupled to a controller; adata collection circuit structured to collect output data from the inputsensors; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternsindicative of an outcome. In embodiments, the machine learning dataanalysis circuit is structured to learn received output data patterns bybeing seeded with a model based on a productivity measure.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, set a parameter of a data collection band for collection by adata collector. The parameter may relate to at least one of setting afrequency range for collection and setting an extent of granularity forcollection.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, identify a set of sensors among a larger set of availablesensors for collection by a data collector. The user interface mayinclude views of available data collectors, their capabilities, one ormore corresponding smart bands, and the like.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, select a set of inputs to be multiplexed among a set ofavailable inputs.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, select a component of an industrial machine displayed in thegraphical user interface for data collection, view a set of sensors thatare available to provide data about the industrial machine, and select asubset of sensors for data collection.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, view a set of indicators of fault conditions of one or moreindustrial machines, where the fault conditions are identified byapplication of an expert system to data collected from a set of datacollectors. In embodiments, the fault conditions may be identified bymanufacturers of portions of the one or more industrial machines. Thefault conditions may be identified by analysis of industry trade data,third-party testing agency data, industry standards, and the like. Inembodiments, a set of indicators of fault conditions of one or moreindustrial machines may include indicators of stress, vibration, heat,wear, ultrasonic signature, operational deflection shape, and the like,optionally including any of the widely varying conditions that can besensed by the types of sensors described throughout this disclosure andthe documents incorporated herein by reference.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface that enablesa user to select from a list of component parts of an industrial machinefor establishing smart-band monitoring and in response thereto presentsthe user with at least one smart-band definition of an acceptable rangeof values for at least one sensor of the industrial machine and a listof correlated sensors from which data will be gathered and analyzed whenan out of acceptable range condition is detected from the at least onesensor.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface that enablesa user to select from a list of conditions of an industrial machine forestablishing smart-band monitoring and, in response thereto, presentsthe user with at least one smart-band definition of an acceptable rangeof values for at least one sensor of the industrial machine and a listof correlated sensors from which data will be gathered and analyzed whenan out of acceptable range condition is detected from the at least onesensor.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface that enablesa user to select from a list of reliability measures of an industrialmachine for establishing smart-band monitoring and, in response thereto,presents the user with at least one smart-band definition of anacceptable range of values for at least one sensor of the industrialmachine and a list of correlated sensors from which data will begathered and analyzed when an out of acceptable range condition isdetected from the at least one sensor. In the system, the reliabilitymeasures may include one or more of industry average data,manufacturer's specifications, material specifications, recommendations,and the like. In embodiments, reliability measures may include measuresthat correlate to failures, such as stress, vibration, heat, wear,ultrasonic signature, operational deflection shape effect, and the like.In embodiments, manufacturer's specifications may include cycle count,working time, maintenance recommendations, maintenance schedules,operational limits, material limits, warranty terms, and the like. Inembodiments, the sensors in the industrial environment may be correlatedto manufacturer's specifications by associating a condition being sensedby the sensor to a specification type. In embodiments, a non-limitingexample of correlating a sensor to a manufacturer's specification mayinclude a duty cycle specification being correlated to a sensor thatdetects revolutions of a moving part. In embodiments, a temperaturespecification may correlate to a thermal sensor disposed to sense anambient temperature proximal to the industrial machine.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface thatautomatically creates a smart-band group of sensors disposed in theindustrial environment in response to receiving a condition of theindustrial environment for monitoring and an acceptable range of valuesfor the condition.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface that presentsa representation of components of an industrial machine deployable inthe industrial environment on an electronic display, and in response toa user selecting one or more of the components, searches a database ofindustrial machine failure modes for modes involving the selectedcomponent(s) and conditions associated with the failure mode(s) to bemonitored, and further identifies a plurality of sensors in, on, oravailable to be disposed on the presented machine representation fromwhich data will automatically be captured when the condition(s) to bemonitored are detected to be outside of an acceptable range. Inembodiments, the identified plurality of sensors includes at least onesensor through which the condition(s) will be monitored.

In embodiments, a system for data collection in an industrialenvironment may include a user interface for working with smart bandsthat may facilitate a user identifying sensors to include in a smartband group of sensors by selecting sensors presented on a map of amachine in the environment. A user may be guided to select among asubset of all possible sensors based on smart band criteria, such astypes of sensor data required for the smart band. A smart band may befocused on detecting trending conditions in a portion of the industrialenvironment; therefore, the user interface may direct the user chooseamong an identified subset of sensors, such as by only allowing sensorsproximal to the smart band directed portion of the environment to beselectable in the user interface.

In embodiments, a smart band data collection configuration anddeployment user interface may include views of components in anindustrial environment and related available sensors. In embodiments, inresponse to selection of a component part of an industrial machinedepicted in the user interface, sensors associated with smart band datacollection for the component part may be highlighted so that the usermay select one or more of the sensors. User selection in this contextmay comprise a verification of an automatic selection of sensors, ormanually identifying sensors to include in the smart band sensor group.

In embodiments, in response to selection of a smart band condition, suchas trending of bearing temperature, a user interface for smart bandconfiguration and use may automatically identify and present sensorsthat contribute to smart band analysis for the condition. A user mayresponsive to this presentation of sensors, confirm or otherwiseacknowledge one or more sensors individually or as a set to be includedin the smart band data collection group.

In embodiments, a smart band user interface may present locations ofindustrial machines in an industrial environment on a map. The locationsmay be annotated with indicators of smart band data collection templatesthat are configured for collecting smart band data for the machines atthe annotated locations. The locations may be color coded to reflect adegree of smart band coverage for a machine at the location. Inembodiments, a location of a machine with a high degree of smart bandcoverage may be colored green, whereas a location of a machine with lowsmart band coverage may be colored red or some other contrasting color.Other annotations, such as visual annotations may be used. A user mayselect a machine at a location and by dragging the selected machine to alocation of a second machine, effectively configure smart bands for thesecond machine that correspond to smart bands for the first machine. Inthis way, a user may configure several smart band data collectiontemplates for a newly added machine or a new industrial environment andthe like.

In embodiments, various configurations and selections of smart bands maybe stored for use throughout a data collection platform, such as forselecting templates for sensing, templates for routing, provisioning ofdevices and the like, as well as for direct the placement of sensors,such as by personnel or by machines, such as autonomous orremote-control drones.

In embodiments, a smart band user interface may present a map of anindustrial environment that may include industrial machines,machine-specific data collectors, mobile data collectors (robotic andhuman), and the like. A user may view a list of smart band datacollection actions to be performed and may select a data collectionresource set to undertake the collection. In an example, a guided mobilerobot may be equipped with data collection systems for collecting datafor a plurality of smart band data sets. A user may view an industrialenvironment with which the robot is associated and assign the robot toperform a smart band data collection activity by selecting the robot, asmart band data collection template, and a location in the industrialenvironment, such as a machine or a part of a machine. The userinterface may provide a status of the collection undertaking so that theuser can be informed when the data collection is complete.

In embodiments, a smart band operation management user interface mayinclude presentation of smart band data collection activity, analysis ofresults, actions taken based on results, suggestions for changes tosmart band data collection (e.g., addition of sensors to a smart bandcollection template, increasing duration of data collection for atemplate-specific collection activity), and the like. The user interfacemay facilitate “what if” type analysis by presenting potential impactson reliability, costs, resource utilization, data collection tradeoffs,maintenance schedule impacts, risk of failure (increase/decrease), andthe like in response to a user's attempt to make a change to a smartband data collection template, such as a user relaxing a threshold forperforming smart band data collection and the like. In embodiments, auser may select or enter a target budget for preventive maintenance perunit time (e.g., per month, quarter, and the like) into the userinterface and an expert system of the user interface may recommend asmart band data collection template and thresholds for complying withthe budget.

In embodiments, a smart band user interface may facilitate a userconfiguring a system for data collection in an industrial environmentfor smart band data gathering. The user interface may include display ofindustrial machine components, such as motors, linkages, bearings, andthe like that a user may select. In response to such a selection, anexpert system may work with the user interface to present a list ofpotential failure conditions related to the part to monitor. The usermay select one or more conditions to monitor. The user interface maypresent the conditions to monitor as a set that the user may be asked toapprove. The user may indicate acceptance of the set or of selectconditions in the set monitor. As a follow-on to a userselection/approval of one or more conditions to monitor, the userinterface may display a map of relevant sensors available in theindustrial environment for collecting data as a smart band group ofsensors. The relevant sensors may be associated with one or more parts(e.g., the part(s) originally selected by the user), one or more failureconditions, and the like.

In embodiments, the expert system may compare the relevant sensors inthe environment to a preferred set of sensors for smart band monitoringof the failure condition(s) and provide feedback to the user, such as aconfidence factor for performing smart band monitoring based on theavailable sensors for the failure condition(s). The user may evaluatethe failure condition and smart band analysis information presented andmay take an action in the user interface, such as approving the relevantsensors. In response, a smart band data collection template forconfiguring the data collection system may be created. In embodiments, asmart band data collection template may be created independently of auser approval. In such embodiments, the user may indicate explicitly orimplicitly via approval of the smart band analysis information anapproval of the created template.

In embodiments, a smart band user interface may work with an expertsystem to present candidate portions of an industrial machine in anindustrial environment for smart band condition monitoring based oninformation such as manufacturer's specifications, statisticalinformation derived from real-world experience with similar industrialmachines, and the like. In embodiments, the user interface may permit auser to select certain aspects of the smart band data collection andanalysis process including—for example, a degree of reliability/failurerisk to monitor (e.g., near failure, best performance, industry average,and the like). In response thereto, the expert system may adjust anaspect of the smart band analysis, such as a range of acceptable valueto monitor, a monitor frequency, a data collection frequency, a datacollection amount, a priority for the data collection activity (e.g.,effectively a priority of a template for data collection for the smartband), weightings of data from sensors (e.g., specific sensors in thegroup, types of sensors, and the like).

In embodiments, a smart bands user interface may be structured to allowa user to let an expert system recommend one or more smart bands toimplement based on a range of comparative data that the user mightprioritize, such as industry average data, industry best data, near-bycomparable machines, most similarly configured machines, and the like.Based on the comparative data weighting, the expert system may use theuser interface to recommend one or more smart band templates that alignwith the weighting to the user, who may take an action in the userinterface, such as approving one or more of the recommended templatesfor use.

In embodiments, a user interface for configuring arrangement of sensorsin an industrial environment may include recommendations by industrialenvironment equipment suppliers (e.g., manufacturers, wholesalers,distributors, dealers, third-party consultants, and the like) ofgroup(s) of sensors to include for performing smart band analysis ofcomponents of the industrial equipment. The information may be presentedto a user as data collection template(s) that the user may indicate asbeing accepted/approved, such as by positioning a graphic representing atemplate(s) over a portion of the industrial equipment.

In embodiments, a smart band discovery portal may facilitate sharing ofsmart band related information, such as recommendations, actual usecases, results of smart band data collection and processing, and thelike. The discovery portal may be embodied as a panel in a smart banduser interface.

In embodiments, a smart band assessment portal may facilitate assessmentof smart band-based data collection and analysis. Content that may bepresented in such a portal may include depictions of uses of existingsmart band templates for one or more industrial machines, industrialenvironments, industries, and the like. A value of a smart band may beascribed to each smart band in the portal based, for example, onhistorical use and outcomes. A smart band assessment portal may alsoinclude visualization of candidate sensors to include in a smart banddata collection template based on a range of factors including ascribedvalue, preventive maintenance costs, failure condition being monitored,and the like.

In embodiments, a smart bands graphical user interface associated with asystem for data collection in an industrial environment may be deployedfor industrial components, such as of factory-based air conditioningunits. A user interface of a system for data collection for smart bandanalysis of air conditioning units may facilitate graphicalconfiguration of smart band data collection templates and the like forspecific air conditioning system installations. In embodiments, majorcomponents of an air conditioning system, such as a compressor,condenser, heat exchanger, ducting, coolant regulators, filters, fans,and the like along with corresponding sensors for a particularinstallation of the air conditioning system may be depicted in a userinterface. A user may select one or more of these components in the userinterface for configuring a system for smart band data collection. Inresponse to the user selecting, for example, a coolant compressor,sensors associated with the compressor may be automatically identifiedin the user interface. The user may be presented with a recommended datacollection template to perform smart band data collection for theselected compressor. Alternatively, the user may request a candidatecollection template from a community of smart band users, such asthrough a smart band template sharing panel of the user interface. Oncea template is selected, the user interface may offer the usercustomization options, such as frequency of collection, degree ofreliability to monitor, and the like. Upon final acceptance of thetemplate, the user interface may interact with a data collection systemof the installed air conditioning system (if such a system is available)to implement the data collection template and provide an indication tothe user of the result of implementing the template. In responsethereto, the user may make a final approval of the template for use withthe air conditioning unit.

In embodiments, a smart bands graphical user interface associated with asystem for data collection in an industrial environment may be deployedfor oil and gas refinery-based chillers. A user interface of a systemfor data collection for smart band analysis of refinery-based chillersmay facilitate graphical configuration of smart band data collectiontemplates and the like for specific refinery-based chillerinstallations. In embodiments, major components of a refinery-basedchiller including heat exchangers, compressors, water regulators and thelike along with corresponding sensors for the particular installation ofthe refinery-based chiller may be depicted in a user interface. A usermay select one or more of these components in the user interface forconfiguring a system for smart band data collection. In response to theuser selecting, for example, water regulators, sensors associated withthe water regulators may be automatically identified in the userinterface. The user may be presented with a recommended data collectiontemplate to perform smart band data collection for the selectedcomponent. Alternatively, the user may request a candidate collectiontemplate from a community of smart band users, such as through a smartband template sharing panel of the user interface. Once a template isselected, the user interface may offer the user customization options,such as frequency of collection, degree of reliability to monitor, andthe like. Upon final acceptance of the template, the user interface mayinteract with a data collection system of the installed refinery-basedchiller (if such a system is available) to implement the data collectiontemplate and provide an indication to the user of the result ofimplementing the template. In response thereto, the user may make afinal approval of the template for use with the refinery-based chiller.

In embodiments, a smart bands graphical user interface associated with asystem for data collection in an industrial environment may be deployedfor automotive production line robotic assembly systems. A userinterface of a system for data collection for smart band analysis ofproduction line robotic assembly systems may facilitate graphicalconfiguration of smart band data collection templates and the like forspecific production line robotic assembly system installations. Inembodiments, major components of a production line robotic assemblysystem including motors, linkages, tool handlers, positioning systemsand the like along with corresponding sensors for the particularinstallation of the production line robotic assembly system may bedepicted in a user interface. A user may select one or more of thesecomponents in the user interface for configuring a system for smart banddata collection. In response to the user selecting, for example, roboticlinkage sensors associated with the robotic linkages may beautomatically identified in the user interface. The user may bepresented with a recommended data collection template to perform smartband data collection for the selected component. Alternatively, the usermay request a candidate collection template from a community of smartband users, such as through a smart band template sharing panel of theuser interface. Once a template is selected, the user interface mayoffer the user customization options, such as frequency of collection,degree of reliability to monitor, and the like. Upon final acceptance ofthe template, the user interface may interact with a data collectionsystem of the installed production line robotic assembly system (if sucha system is available) to implement the data collection template andprovide an indication to the user of the result of implementing thetemplate. In response thereto, the user may make a final approval of thetemplate for use with the production line robotic assembly system.

In embodiments, a smart bands graphical user interface associated with asystem for data collection in an industrial environment may be deployedfor automotive production line robotic assembly systems. A userinterface of a system for data collection for smart band analysis ofproduction line robotic assembly systems may facilitate graphicalconfiguration of smart band data collection templates and the like forspecific production line robotic assembly system installations. Inembodiments, major components of construction site boring machinery,such as the cutter head, which itself is a subsystem that may have manycomponents, control systems, debris handling and conveying components,precast concrete delivery and installation subsystems and the like alongwith corresponding sensors for the particular installation of theproduction line robotic assembly system may be depicted in a userinterface. A user may select one or more of these components in the userinterface for configuring a system for smart band data collection. Inresponse to the user selecting, for example, debris handling componentssensors associated with the debris handling components, such as aconveyer may be automatically identified in the user interface. The usermay be presented with a recommended data collection template to performsmart band data collection for the selected component. Alternatively,the user may request a candidate collection template from a community ofsmart band users, such as through a smart band template sharing panel ofthe user interface. Once a template is selected, the user interface mayoffer the user customization options, such as frequency of collection,degree of reliability to monitor, and the like. Upon final acceptance ofthe template, the user interface may interact with a data collectionsystem of the installed production line robotic assembly system (if sucha system is available) to implement the data collection template andprovide an indication to the user of the result of implementing thetemplate. In response thereto, the user may make a final approval of thetemplate for use with the production line robotic assembly system.

Referring to FIG. 149, an exemplary user interface for smart bandconfiguration of a system for data collection in an industrialenvironment is depicted. The user interface 11200 may include anindustrial environment visualization portion 11202 in which may bedepicted one or more sensors, machines, and the like. Each sensor,machine, or portion thereof (e.g., motor, compressor, and the like) maybe selectable as part of a smart-band configuration process. Likewise,each sensor, machine or portion thereof may be visually highlightedduring the smart-band configuration process, such as in response to userselection, or automated identification as being part of a group of smartband sensors. The user interface may also include a smart band selectionportion 11204 or panel in which smart band indicators, failure modes,and the like may be depicted in selectable elements. User selection of asymptom, failure mode and the like may cause corresponding components,sensors, machines, and the like in the industrial visualization portionto be highlighted. The user interface may also include a customizationpanel 11206 in which attributes of a selected smart band, such asacceptable ranges, frequency of monitoring and the like may be madeavailable for a user to adjust.

In embodiments, methods and systems disclosed herein include a systemhaving a user interface including a selectable graphical element thatfacilitates selection of a representation of a component of anindustrial machine from an industrial environment in which a pluralityof sensors is deployed from which a data collection system collects datafor the system for which the user interface enables interaction; andselectable graphical elements representing a portion of the plurality ofsensors that facilitate selection of a sensors to form a data collectionsubset of sensors in the industrial environment. In embodiments,selection of sensors to form a data collection subset results in a datacollection template adapted to facilitate configuring the data routingand collection system for collecting data from the data collectionsubset of sensors. In embodiments, the user interface comprises anexpert system that analyzes a user selection of a graphical element thatfacilitates selection of a component and adjusts the selectablegraphical elements representing a portion of the plurality of sensors toactivate only sensors associated with a component associated with theselected graphical element. In embodiments, the selectable graphicalelement that facilitates selection of a component of an industrialmachine further facilitates presentation of a plurality of datacollection templates associated with the component. In embodiments, theportion of the plurality of sensors comprises a smart band group ofsensors. In embodiments, the smart band group of sensors comprisessensors for a component of the industrial machine selected by theselectable graphical element.

In embodiments, methods and systems disclosed herein include a systemhaving an expert graphical user interface including representations of aplurality of components of an industrial machine from an industrialenvironment in which a plurality of sensors is deployed from which adata collection system collects data for the system for which the userinterface enables interaction. In embodiments, at least onerepresentation of the plurality of components is selectable by a user inthe user interface; a database of industrial machine failure modes; anda database searching facility that searches the database of failuremodes for modes that correspond to a user selection of a component ofthe plurality of components. In embodiments, the system includes adatabase of conditions associated with the failure modes. Inembodiments, the database of conditions includes a list of sensors inthe industrial environment associated with the condition. Inembodiments, the database searching facility further searches thedatabase of conditions for sensors that correspond to at least onecondition and indicates the sensors in the graphical user interface. Inembodiments, the user selection of a component of the plurality ofcomponents causes a data collection template for configuring the datarouting and collection system to automatically collect data from sensorsassociated with the selected component.

In embodiments, methods and system disclosed herein include an exemplarymethod comprising presenting in an expert graphical user interface alist of reliability measures of an industrial machine; facilitating userselection of one reliability measure from the list; presenting arepresentation of a smart band data collection template associated withthe selected reliability measure; and in response to a user indicationof acceptance of the smart band data collection template, configuring adata routing and collection system to collect data from a plurality ofsensors in an industrial environment in response to a data value fromone of the plurality of sensors being detected outside of an acceptablerange of data values. In embodiments, the reliability measures includeone or more of industry average data, manufacturer's specifications,manufacturer's material specifications, and manufacturer'srecommendations. In embodiments, including the manufacturer'sspecifications includes at least one of cycle count, working time,maintenance recommendations, maintenance schedules, operational limits,material limits, and warranty terms. In embodiments, the reliabilitymeasures correlate to failures selected from the list consisting ofstress, vibration, heat, wear, ultrasonic signature, and operationaldeflection shape effect. In embodiments, the method includes correlatingsensors in the industrial environment to manufacturer's specifications.In embodiments, correlating comprises matching a duty cyclespecification to a sensor that detects revolutions of a moving part. Inembodiments, correlating comprises matching a temperature specificationwith a thermal sensor disposed to sense an ambient temperature proximalto the industrial machine. In embodiments, the method includesdynamically setting the acceptable range of data values based on aresult of the correlating. In embodiments, the method includesautomatically determining the one of the plurality of sensors fordetecting the data value outside of the acceptable range based on aresult of the correlating.

Back calculation, such as for determining possible root causes offailures and the like, may benefit from a graphical approach thatfacilitates visualizing an industrial environment, machine, or portionthereof marked with indications of information sources that may providedata such as sensors and the like related to the failure. A failed part,such as a bearing, may be associated with other parts, such as shaft,motor, and the like. Sensors for monitoring conditions of the bearingand the associated parts may provide information that could indicate apotential source of failure. Such information may also be useful tosuggest indicators, such as changes in sensor output, to monitor oravoid the failure in the future. A system that facilitates a graphicalapproach for back-calculation may interact with sensor data collectionand analysis systems to at least partially automate aspects related todata collection and processing determined from a back-calculationprocess.

In embodiments, a system for data collection in an industrialenvironment may include a user interface in which portions of anindustrial machine associated with a condition of interest, such as afailure condition, are presented on an electronic display along withsensor data types contributing to the condition of interest, datacollection points (e.g., sensors) associated with the machine portionsthat monitor the data types, a set of data from the data collectionpoints that was collected and used to determine the condition ofinterest, and an annotation of sensors that delivered exceptional data,such as data that is out of an acceptable range, and the like, that mayhave been used to determine the condition of interest. The userinterface may access a description of the machine that facilitatesdetermining and visualizing related components, such as bearing, shafts,brakes, rotors, motor housings, and the like that contribute to afunction, such as rotating a turbine. The user interface may also accessa data set that relates sensors disposed in and about the machine withthe components. Information in the data set may include descriptions ofthe sensors, their function, a condition that each senses, typical oracceptable ranges of values output from the sensors, and the like. Theinformation in the data set may also identify a plurality of potentialpathways in a system for data collection in an industrial environmentfor sensor data to be delivered to a data collector. The user interfacemay also access a data set that may include data collection templatesused to configure a data collection system for collecting data from thesensors to meet specific purposes (e.g., to collect data from groups ofsensors into a sensor data set suitable for determining a condition ofthe machine, such as a degree of slippage of the shaft relative to themotor, and the like).

In embodiments, a method of back-calculation for determining candidatesources of data collection for data that contributes to a condition ofan industrial machine may include following routes of data collectiondetermined from a configuration and operational template of a datacollection system for collecting data from sensors deployed in theindustrial machine that was in place when the contributing data wascollected. A configuration and operational template may describe signalpath switching, multiplexing, collection timing, and the like for datafrom a group of sensors. The group of sensors may be local to acomponent, such as a bearing, or more regionally distributed, such assensors that capture information about the bearing and its relatedcomponents. In embodiments, a data collection template may be configuredfor collecting and processing data to detect a particular condition ofthe industrial machine. Therefore, templates may be correlated toconditions so that performing back-calculation of a condition ofinterest can be guided by the correlated template. Data collected basedon the template may be examined and compared to acceptable ranges ofdata for various sensors. Data that is outside of an acceptable rangemay indicate potential root causes of an unacceptable condition. Inembodiments, a suspect source of data collection may be determined fromthe candidate sources of data collection based on a comparison of datacollected from the candidate data sources with an acceptable range ofdata collected from each candidate data source. Visualizing theseback-calculation based signal paths, candidate sensors, and suspect datasources provides a user with valuable insights into possible root causesof failures and the like.

In embodiments, a method for back-calculation may include visualizingroute(s) of data that contribute to a fault condition detected in anindustrial environment by applying back-calculation to determine sourcesof the contributed data with the visualizing appearing as highlighteddata paths in a visual representation of the data collection system inthe industrial machine. In embodiments, determining sources of data maybe based on a data collection and processing template for the faultcondition. The template may include a configuration of a data collectionsystem when data from the determined sources was collected with thesystem.

When failures occur, or conditions of a portion of a machine in anindustrial environment reach a critical point prior to failure, such asmay be detected during preventive maintenance and the like,back-calculation may be useful in determining information to gather thatmight help avoid the failure and/or improve system performance—forexample, by avoiding substantive degradation in component operation.Visualizing data collection sources, components related to a condition,algorithms that may determine the potential onset of the condition andthe like may facilitate preparation of data collection templates forconfiguring data sensing, routing, and collection resources in a systemfor data collection in an industrial environment. In embodiments,configuring a data collection template for a system for collecting datain an industrial environment may be based on back-calculations appliedto machine failures that identify candidate conditions to monitor foravoiding the machine failures. The resulting template may identifysensors to monitor, sensor data collection path configuration,frequency, and amount of data to collect, acceptable levels of sensordata, and the like. With access to information about the machine, suchas which parts closely relate to others and sensors that collected datafrom parts in the machine, a data collection system configurationtemplate may be automatically generated when a target component isidentified.

In embodiments, a user interface may include a graphical display of datasources as a logical arrangement of sensors that may contribute data toa calculation of a condition of a machine in an industrial environment.A logical arrangement may be based on sensor type, data collectiontemplate, condition, algorithm for determining a condition, and thelike. In an example, a user may wish to view all temperature sensorsthat may contribute to a condition, such as a failure of a part in anindustrial environment. A user interface may communicate with a databaseof machine related information, such as parts that relate to acondition, sensors for those parts, and types of those sensors todetermine the subset of sensors that measure temperature. The userinterface may highlight those sensors. The user interface may activateselectable graphical elements for those sensors that, when selected bythe user, may present data associated with those sensors, such as sensortype, ranges of data collected, acceptable ranges, actual data valuescollected for a given condition, and the like, such as in a pop-up panelor the like. Similar functionality of the user interface may apply tophysical arrangements of sensors, such as all sensors associated with amotor, boring machine cutting head, wind turbine, and the like.

In embodiments, third-parties, such as component manufacturers, remotemaintenance organizations and the like may benefit from access toback-calculation visualization. Permitting third parties to have accessto back-calculation information, such as sensors that contributedunacceptable data values to a calculation of a condition, visualizationof sensor positioning, and the like may be an option that a user canexercise in a user interface for a graphical approach toback-calculations as described herein. A list of manufacturers ofmachines, sub-systems, individual components, sensors, data collectionsystems, and the like may be presented along with remote maintenanceorganizations, and the like in a portion of a user interface. A user ofthe interface may select one or more of these third-parties to grantaccess to at least a portion of the available data and visualizations.Selecting one or more of these third-parties may also presentstatistical information about the party, such occurrences and frequencyof access to data to which the party is granted access, request from theparty for access, and the like.

In embodiments, visualization of back-calculation analysis may becombined with machine learning so that back-calculations and theirvisualizations may be used to learn potential new diagnoses forconditions, such as failure conditions, to learn new conditions tomonitor, and the like. A user may interact with the user interface toprovide the machine learning techniques feedback to improve results,such as indicating a success or failure of an attempt to preventfailures through specific data collection and processing solutions(e.g., templates), and the like.

In embodiments, methods and systems of back-calculation of datacollected with a system for data collection in an industrial environmentmay be applied to concrete pouring equipment in a construction siteapplication. Concrete pouring equipment may comprise several activecomponents including mixers that may include water and aggregate supplysystems, mixing control systems, mixing motors, directional controllers,concrete sensors and the like, concrete pumps, delivery systems, flowcontrol as well as on/off controls, and the like. Back-calculation offailure or other conditions of active or passive components of aconcrete pouring equipment may benefit from visualization of theequipment, its components, sensors, and other points where data iscollected (e.g., controllers and the like). Visualizing data/conditionscollected from sensors associated with concrete pumps and the like whenperforming back-calculation of a flow rate failure condition may informthe user of a conditions of the pump that may contribute to the flowrate failure. Flow rate may decrease contemporaneously with an increasein temperature of the pump. This may be visualized by, for example,presenting the flow rate sensor data and the pump temperature sensordata in the user interface. This correlation may be noted by an expertsystem or by a user observing the visualization and corrective actionmay be taken.

In embodiments, methods and systems of back-calculation of datacollected with a system for data collection in an industrial environmentmay be applied to digging and extraction systems in a miningapplication. Digging and extraction systems may comprise several activesub-systems including cutting heads, pneumatic drills, jack hammers,excavators, transport systems, and the like. Back-calculation of failureor other conditions of active or passive components of digging andextraction systems may benefit from visualization of the equipment, itscomponents, sensors, and other points where data is collected (e.g.,controllers and the like). Visualizing data/conditions collected fromsensors associated with pneumatic drills and the like when performingback-calculation of a pneumatic line failure condition may inform theuser of a conditions of the drill that may contribute to the linefailure. Line pressure may increase contemporaneously with a change of acondition of the drill. This may be visualized by, for example,presenting the line pressure sensor data and data from sensorsassociated with the drill in the user interface. This correlation may benoted by an expert system or by a user observing the visualization andcorrective action may be taken.

In embodiments, methods and systems of back-calculation of datacollected with a system for data collection in an industrial environmentmay be applied to cooling towers in an oil and gas productionenvironment. Cooling towers may comprise several active componentsincluding feedwater systems, pumps, valves, temperature-controlledoperation, storage systems, mixing systems, and the like.Back-calculation of failure or other conditions of active or passivecomponents of cooling towers may benefit from visualization of theequipment, its components, sensors and other points where data iscollected (e.g., controllers and the like). Visualizing data/conditionscollected from sensors associated with the cooling towers and the likewhen performing back-calculation of a circulation pump failure conditionmay inform the user of a conditions of the cooling towers that maycontribute to the pump failure. Temperature of the feedwater mayincrease contemporaneously with a decrease in output of the circulationpump. This may be visualized by, for example, presenting the feed watertemperature sensor data and the pump output rate sensor data in the userinterface. This correlation may be noted by an expert system or by auser observing the visualization and corrective action may be taken.

In embodiments, methods and systems of back-calculation of datacollected with a system for data collection in an industrial environmentmay be applied to circulation water systems in a power generationapplication. Circulation water systems may comprise several activecomponents including, pumps, storage systems, water coolers, and thelike. Back-calculation of failure or other conditions of active orpassive components of circulation water systems may benefit fromvisualization of the equipment, its components, sensors and other pointswhere data is collected (e.g., controllers and the like). Visualizingdata/conditions collected from sensors associated with water coolers andthe like when performing back-calculation of a circulation watertemperature failure condition may inform the user of a conditions of thecooler that may contribute to the temperature condition failure.Circulation temperature may increase contemporaneously with an increaseof core water cooler temperature. This may be visualized by, forexample, presenting the circulation water temperature sensor data andthe water cooler temperature sensor data in the user interface. Thiscorrelation may be noted by an expert system or by a user observing thevisualization and corrective action may be taken.

Referring to FIG. 150 a graphical approach 11300 for back-calculation isdepicted. Components of an industrial environment may be depicted in amap of the environment 11302. Components that may have a history offailure (with this installation or others) may be highlighted. Inresponse to a selection of one of these components (such as by a usermaking the selection), related components and sensors for the selectedpart and related components may be highlighted, including signal routingpaths for the data from their relevant sensors to a data collector.Additional highlighting may be added to sensors from which unacceptabledata has been collected, thereby indicating potential root causes of afailure of the selected part. The relationships among the parts may bebased at least in part on machine configuration metadata. Therelationship between specific sensors and the failure condition may bebased at least in part on a data collection template associated with thepart and/or associated with the failure condition.

In embodiments, methods and systems disclosed herein includes a systemhaving a user interface of a system adapted to collect data in anindustrial environment; the user interface including a plurality ofgraphical elements representing mechanical portions of an industrialmachine. In embodiments, the plurality of graphical elements isassociated with a condition of interest generated by a processorexecuting a data analysis algorithm; a plurality of graphical elementsrepresenting data collectors in a system adapted for collecting data inan industrial environment that collected data used in the data analysisalgorithm; and a plurality of graphical elements representing sensorsused to capture the data used in the data analysis algorithm. Inembodiments, graphical elements for sensors that provided data that wasoutside of an acceptable range of data values are indicated through avisual highlight in the user interface. In embodiments, the condition ofinterest is selected from a list of conditions of interest presented inthe user interface. In embodiments, the condition of interest is amechanical failure of at least one of the mechanical portions of theindustrial machine. In embodiments, the mechanical portions comprise atleast one of a bearing, shaft, rotor, housing, and linkage of theindustrial machine. In embodiments, the acceptable range of data valuesis available for each sensor. In embodiments, the system includeshighlighting data collectors that collected the data that was outside ofthe acceptable range of data values. In embodiments, the system includesa data collection system configuration template that facilitatesconfiguring the data collection system to collect the data forcalculating the condition of interest.

In embodiments, the methods and systems include a method of determiningcandidate sources of a condition of interest. The method includesidentifying a data collection template for configuring data routing andcollection resources in a system adapted to collect data in anindustrial environment. In embodiments, the template was used to collectdata that contributed to a calculation of the condition of interest. Themethod includes determining paths from data collectors for the collecteddata to sensors that produced the collected data by analyzing the datacollection template; comparing data collected by the sensors withacceptable ranges of data values for data collected by the sensors; andhighlighting, in an electronic user interface that depicts theindustrial environment and at least one of the sensors, at least onesensor that produced data that contributed to the calculation of thecondition of interest that is outside of the acceptable range of datafor that sensor. In embodiments, the condition of interest is a failurecondition. In embodiments, the data collection template comprisesconfiguration information for at least one of an analog crosspointswitch, a multiplexer, a hierarchical multiplexer, a sensor, acollector, and a data storage facility of the system adapted to collectdata in the industrial environment. In embodiments, the highlighting inthe industrial environment comprises highlighting the at least onesensor, and at least one route of data from the sensor to a datacollector of the system for data collection in the industrialenvironment. In embodiments, comparing data collected by the sensorswith acceptable ranges of data values comprises comparing data collectedby each sensor with an acceptable range of data values that is specificto each sensor. In embodiments, the calculation of the condition ofinterest comprises calculating a trend of data from at least one sensor.In embodiments, the acceptable range of values comprises a trend of datavalues.

In embodiments, methods and systems include a method of visualizingroutes of data that contribute to a condition of interest that isdetected in an industrial environment. The method includes applying backcalculation to the condition of interest to determine a data collectionsystem configuration template associated with the condition of interest;analyzing the template to determine a configuration of the datacollection system for collecting data for detecting the condition ofinterest; presenting, in an electronic user interface, a map of the datacollection configured by the template; and highlighting, in theelectronic user interface, routes in the data collection system thatreflect paths of data from at least one sensor to at least one datacollector for data that contributes to calculating the condition ofinterest. In embodiments, the data collection system configurationtemplate comprises configuration information for at least one resourcedeployed in the data collection system selected from the list consistingof an analog crosspoint switch, a multiplexer, a hierarchicalmultiplexer, a data collector, and a sensor. In embodiments, the methodincludes generating a target diagnosis for the condition of interest byapplying machine learning to the back calculation. In embodiments, themethod includes highlighting in the electronic user interface, sensorsthat produce data used in calculating the condition of interest that isoutside of an acceptable range of data values for the sensor. Inembodiments, the condition of interest is selected from a list ofconditions of interest presented in the user interface. In embodiments,the condition of interest is a mechanical failure of at least onemechanical portion of the industrial environment. In embodiments, themechanical portions comprise at least one of a bearing, shaft, rotor,housing, and linkage of the industrial environment.

In embodiments, a system for data collection in an industrialenvironment may route data from a plurality of sensors in the industrialenvironment to wearable haptic stimulators that present the data fromthe sensors as human detectable stimuli including at least one oftactile, vibration, heat, sound, and force. In embodiments, the hapticstimulus represents an effect on the machine resulting from the senseddata. In embodiments, a bending effect may be presented as bending afinger of a haptic glove. In embodiments, a vibrating effect may bepresented as vibrating a haptic arm band. In embodiments, a heatingeffect may be presented as an increase in temperature of a haptic wristband. In embodiments, an electrical effect (e.g., over voltage, current,and others) may be presented as a change in sound of a phatic audiosystem.

In embodiments, an industrial machine operator haptic user interface maybe adapted to provide haptic stimuli to the operator that is responsiveto the operator's control of the machine. In embodiments, the stimuliindicate an impact on the machine as a result of the operator's controland interaction with objects in the environment as a result thereof. Inembodiments, sensed conditions of the machine that exceed an acceptablerange may be presented to the operator through the haptic userinterface. In embodiments, the sensed conditions of the machine that arewithin an acceptable range may not be presented to the operator throughthe haptic user interface. In embodiments, the sensed conditions of themachine that are within an acceptable range may presented as naturallanguage representations of confirmation of the operator control. Inembodiments, at least a portion of the haptic user interface is worn bythe operator. In embodiments, a wearable haptic user interface devicemay include force exerting devices along the outer legs of a deviceoperator's uniform. When a vehicle that the operator is controllingapproaches an obstacle along a lateral side of the vehicle, aninflatable bellows may be inflated, exerting pressure against the leg ofthe operator closest to the side of the vehicle approaching theobstacle. The bellows may continue to be inflated, thereby exertingadditional pressure on the operator's leg that is consistent with theproximity of the obstacle. The pressure may be pulsed when contact withthe obstacle is imminent. In another example, an arm band of an operatormay vibrate in coordination with vibration being experienced by aportion of the vehicle that the operator is controlling. These aremerely examples and not intended to be limiting or restrictive of theways in which a wearable haptic feedback user device may be controlledto indicate conditions that are sensed by a system for data collectionin an industrial environment.

In embodiments, a haptic user interface safety system worn by a user inan industrial environment may be adapted to indicate proximity to theuser of equipment in the environment by stimulating a portion of theuser with at least one of pressure, heat, impact, electrical stimuli andthe like, the portion of the user being stimulated may be closest to theequipment. In embodiments, at least one of the type, strength, duration,and frequency of the stimuli is indicative of a risk of injury to theuser.

In embodiments, a wearable haptic user interface device, that may beworn by a user in an industrial environment, may broadcast its locationand related information upon detection of an alert condition in theindustrial environment. The alert condition may be proximal to the userwearing the device, or not proximal but related to the user wearing thedevice. A user may be an emergency responder, so the detection of asituation requiring an emergency responded, the user's haptic device maybroadcast the user's location to facilitate rapid access to the user orby the user to the emergency location. In embodiments, an alertcondition may be determined from monitoring industrial machine sensorsmay be presented to the user as haptic stimuli, with the severity of thealert corresponding to a degree of stimuli. In embodiments, the degreeof stimuli may be based on the severity of the alert, the correspondingstimuli may continue, be repeated, or escalate, optionally includingactivating multiple stimuli concurrently, send alerts to additionalhaptic users, and the like until an acceptable response is detected,e.g., through the haptic UI. The wearable haptic user device may beadapted to communicate with other haptic user devices to facilitatedetecting the acceptable response.

In embodiments, a wearable haptic user interface for use in anindustrial environment may include gloves, rings, wrist bands, watches,arm bands, head gear, belts, necklaces, shirts (e.g., uniform shirt),foot wear, pants, ear protectors, safety glasses, vests, overalls,coveralls, and any other article of clothing or accessory that can beadapted to provide haptic stimuli.

In embodiments, wearable haptic device stimuli may be correlated to asensor in an industrial environment. Non-limiting examples include avibration of a wearable haptic device in response to vibration detectedin an industrial environment; increasing or decreasing the temperatureof a wearable haptic device in response to a detected temperature in anindustrial environment; producing sound that changes in pitchresponsively to changes in a sensed electrical signal, and the like. Inembodiments, a severity of wearable haptic device stimuli may correlateto an aspect of a sensed condition in the industrial environment.Non-limiting examples include moderate or short-term vibration for a lowdegree of sensed vibration; strong or long-term vibration stimulationfor an increase in sensed vibration; aggressive, pulsed, and/ormulti-mode stimulation for a high amount of sensed vibration. Wearablehaptic device stimuli may also include lighting (e.g., flashing, colorchanges, and the like), sound, odor, tactile output, motion of thehaptic device (e.g., inflating/deflating a balloon, extension/retractionof an articulated segment, and the like), force/impact, and the like.

In embodiments, a system for data collection in an industrialenvironment may interface with wearable haptic feedback user devices torelay data collected from fuel handling systems in a power generationapplication to the user via haptic stimulation. Fuel handling for powergeneration may include solid fuels, such as woodchips, stumps, forestresidue, sticks, energy willow, peat, pellets, bark, straw, agrobiomass, coal, and solid recovery fuel. Handling systems may includereceiving stations that may also sample the fuel, preparation stationsthat may crush or chip wood-based fuel or shred waste-based fuel. Fuelhandling systems may include storage and conveying systems, feed and ashremoval systems and the like. Wearable haptic user interface devices maybe used with fuel handling systems by providing an operator feedback onconditions in the handling environment that the user is otherwiseisolated from. Sensors may detect operational aspects of a solid fuelfeed screw system. Conditions like screw rotational rate, weight of thefuel, type of fuel, and the like may be converted into haptic stimuli toa user while allowing the user to use his hands and provide hisattention to operate the fuel feed system.

In embodiments, a system for data collection in an industrialenvironment may interface with wearable haptic feedback user devices torelay data collected from suspension systems of a truck and/or vehicleapplication to the user via haptic stimulation. Haptic simulation may becorrelated with conditions being sensed by the vehicle suspensionsystem. In embodiments, road roughness may be detected and convertedinto vibration-like stimuli of a wearable haptic arm band. Inembodiments, suspension forces (contraction and rebound) may beconverted into stimuli that present a scaled down version of the forcesto the user through a wearable haptic vest.

In embodiments, a system for data collection in an industrialenvironment may interface with wearable haptic feedback user devices torelay data collected from hydroponic systems in an agricultureapplication to the user via haptic stimulation. In embodiments, sensorsin hydroponic systems, such as temperature, humidity, water level, plantsize, carbon dioxide/oxygen levels, and the like may be converted towearable device haptic stimuli. As an operator wearing haptic feedbackclothing walks through a hydroponic agriculture facility, sensorsproximal to the operator may signal to the haptic feedback clothingrelevant information, such as temperature or a measure of actualtemperature versus desired temperature that the haptic clothing mayconvert into haptic stimuli. In an example, a wrist band may include athermal stimulator that can change temperature quickly to tracktemperature data or a derivative thereof from sensors in the agricultureenvironment. As a user walks through the facility, the haptic feedbackwristband may change temperature to indicate a degree to which proximaltemperatures are complying with expected temperatures.

In embodiments, a system for data collection in an industrialenvironment may interface with wearable haptic feedback user devices torelay data collected from robotic positioning systems in an automatedproduction line application to the user via haptic stimulation. Hapticfeedback may include receiving a positioning system indicator ofaccuracy and converting it to an audible signal when the accuracy isacceptable, and another type of stimuli when the accuracy is notacceptable.

Referring to FIG. 151, a wearable haptic user interface device forproviding haptic stimuli to a user that is responsive to data collectedin an industrial environment by a system adapted to collect data in theindustrial environment is depicted. A system for data collection 11402in an industrial environment 11400 may include a plurality of sensors.Data from those sensors may be collected and analyzed by a computingsystem. A result of the analysis may be communicated wirelessly to oneor more wearable haptic feedback stimulators 11404 worn by a userassociated with the industrial environment. The wearable haptic feedbackstimulators may interpret the result, convert it into a form of stimulibased on a haptic stimuli-to-sensed condition mapping, and produce thestimuli.

In embodiments, methods and systems disclosed herein include a systemfor data collection in an industrial environment. The system includes aplurality of wearable haptic stimulators that produce stimuli selectedfrom the list of stimuli consisting of tactile, vibration, heat, sound,force, odor, and motion; a plurality of sensors deployed in theindustrial environment to sense conditions in the environment; aprocessor logically disposed between the plurality of sensors and thewearable haptic stimulators, the processor receiving data from thesensors representative of the sensed condition, determining at least onehaptic stimulation that corresponds to the received data, and sending atleast one signal for instructing the wearable haptic stimulators toproduce the at least one stimulation. In embodiments, the hapticstimulation represents an effect on a machine in the industrialenvironment resulting from the condition. In embodiments, a bendingeffect is presented as bending a haptic device. In embodiments, avibrating effect is presented as vibrating a haptic device. Inembodiments, a heating effect is presented as an increase in temperatureof a haptic device. In embodiments, an electrical effect is presented asa change in sound produced by a haptic device. In embodiments, at leastone of the plurality of wearable haptic stimulators are selected fromthe list consisting of a glove, ring, wrist band, wrist watch, arm band,head gear, belt, necklace, shirt, foot wear, pants, overalls, coveralls,and safety goggles. In embodiments, the at least one signal comprises analert of a condition of interest in the industrial environment. Inembodiments, the at least one stimulation produced in response to thealert signal is repeated by at least one of the plurality of wearablehaptic stimulators until an acceptable response is detected.

In embodiments, systems and methods disclosed herein include anindustrial machine operator haptic user interface that is adapted toprovide the operator haptic stimuli responsive to the operator's controlof the machine based on at least one sensed condition of the machinethat indicates an impact on the machine as a result of the operator'scontrol and interaction with objects in the environment as a resultthereof. In embodiments, a sensed condition of the machine that exceedsan acceptable range of data values for the condition is presented to theoperator through the haptic user interface. In embodiments, a sensedcondition of the machine that is within an acceptable range of datavalues for the condition is presented as natural languagerepresentations of confirmation of the operator control via an audiohaptic stimulator. In embodiments, at least a portion of the haptic userinterface is worn by the operator. 14. In embodiments, a vibratingsensed condition is presented as vibrating stimulation by the hapticuser interface. In embodiments, a temperature-based sensed condition ispresented as heat stimulation by the haptic user interface. 16.

In embodiments, a haptic user interface safety system worn by a user inan industrial environment is adapted to indicate proximity to the userof equipment in the environment by haptic stimulation via a portion ofthe haptic user interface that is closest to the equipment. Inembodiments, at least one of the type, strength, duration, and frequencyof the stimulation is indicative of a risk of injury to the user. Inembodiments, the haptic stimulation is selected from a list consistingof pressure, heat, impact, and electrical stimulation. In embodiments,the haptic user interface further comprises a wireless transmitter thatbroadcasts a location of the user. In embodiments, the wirelesstransmitter broadcasts a location of the user in response to indicatingproximity of the user to the equipment. In embodiments, the proximity tothe user of equipment in the environment is based on sensor dataprovided to the haptic user interface from a system adapted to collectdata in an industrial environment. In embodiments, the system is adaptedbased on a data collection template associated with a user safetycondition in the industrial environment.

In embodiments, a system for data collection in an industrialenvironment may facilitate presenting a graphical element indicative ofindustrial machine sensed data on an augmented reality (AR) display. Thegraphical element may be adapted to represent a position of the senseddata on a scale of acceptable values of the sensed data. The graphicalelement may be positioned proximal to a sensor detected in the field ofview being augmented that captured the sensed data in the AR display.The graphical element may be a color and the scale may be a color scaleranging from cool colors (e.g., greens, blues) to hot colors (e.g.,yellow, red) and the like. Cool colors may represent data values closerto the midpoint of the acceptable range and the hot colors representingdata values close to or outside of a maximum or minimum value of therange.

In embodiments, a system for data collection in an industrialenvironment may present, in an AR display, data being collected from aplurality of sensors in the industrial environment as one of a pluralitygraphical effects (e.g., colors in a range of colors) that correlate thedata being collected from each sensor to a scale of values within anacceptable range compared to values outside of the acceptable range. Inembodiments, the plurality of graphical effects may overlay a view ofthe industrial environment and placement of the plurality of graphicaleffects may correspond to locations in the view of the environment atwhich a sensor is located that is producing the corresponding sensordata. In embodiments, a first set of graphical effects (e.g., hotcolors) represent components for which multiple sensors indicate valuesoutside acceptable ranges.

In embodiments, a system for data collection in an industrialenvironment may facilitate presenting, in an AR display informationbeing collected by sensors in the industrial environment as a heat mapoverlaying a visualization of the environment so that regions of theenvironment with sensor data suggestive of a greater potential offailure are overlaid with a graphic effect that is different thanregions of the environment with sensor data suggestive of a lesserpotential of failure. In embodiments, the heat map is based on datacurrently being sensed. In embodiments, the heat map is based on datafrom prior failures. In embodiments, the heat map is based on changes indata from an earlier period, such as data that suggest an increasedlikelihood of machine failure. In embodiments, the heat map is based ona preventive maintenance plan and a record of preventive maintenance inthe industrial environment.

In embodiments, a system for data collection in an industrialenvironment may facilitate presenting information being collected bysensors in the industrial environment as a heat map overlaying a view ofthe environment, such as a live view as may be presented in an ARdisplay. Such a system may include presenting an overlay thatfacilitates a call to action. In embodiments, the overlay is associatedwith a region of the heat map. The overlay may comprise a visual effectof a part or subsystem of the environment on which the action is to beperformed. In embodiments, the action to be performed is maintenancerelated and may be part-specific.

In embodiments, a system for data collection in an industrialenvironment may facilitate updating, in an AR view of a portion of theenvironment, a heat map of aspects of the industrial environment basedon a change to operating instructions for at least one aspect of amachine in the industrial environment. The heat map may representcompliance with operational limits for portions of machines in theindustrial environment. In embodiments, the heat map may represent alikelihood of component failure as a result of the change to operationinstructions.

In embodiments, a system for data collection in an industrialenvironment may facilitate presenting, as a heat map in an AR view of aportion of the environment, a degree or measure of coverage of sensorsin the industrial environment for a data collection template thatidentifies select sensors in the industrial environment for a datacollection activity.

In embodiments, a system for data collection in an industrialenvironment may facilitate displaying a heat map overlaying a view, suchas a live view, of an industrial environment of failure-related data forvarious portions of the environment. The failure-related data maycomprise a difference between an actual failure rate of the variousportions and another failure rate. Another failure rate may be a rate offailure of comparable portions elsewhere in the environment, and/oraverage failure rate of comparable portions across a plurality ofenvironments, such as an industry average, manufacturer failure rateestimate, and the like.

In embodiments, a system for data collection in an industrialenvironment may facilitate displaying a heat map related to datacollected from robotic arms and hands for production line robotichandling in an augmented reality view of a portion of the environment. Aheat map related to data collected from robotic arms and hands mayrepresent data from sensors disposed in—for example, the fingers of arobotic hand. Sensors may collect data, such as applied pressure whenpinching an object, resistance (e.g., responsive to a robotic touch) ofan object, multi-axis forces presented to the finger as it performs anoperation, such as holding a tool and the like, temperature of theobject, total movement of the finger from initial point of contact untila resistance threshold is met, and other hand position/use conditions.Heat maps of this data may be presented in an augmented reality view ofa robotic production environment so that a user may make a visualassessment of, for example, how the relative positioning of the roboticfingers impacts the object being handled.

In embodiments, a system for data collection in an industrialenvironment may facilitate displaying a heat map related to datacollected from linear bearings for production line robotic handling inan augmented reality view of a portion of the environment. Linearbearings, as with most bearings, may not be visible while in use.However, assessing their operation may benefit from representing datafrom sensors that capture information about the bearings while in use inan augmented reality display. In embodiments, sensors may be placed todetect forces being placed on portions of the bearings by the rotatingmember or elements that the bearings support. These forces may bepresented as heat maps that correspond to relative forces, on avisualization of the bearings in an augmented reality view of a robothandling machine that uses linear bearings.

In embodiments, a system for data collection in an industrialenvironment may facilitate displaying a heat map related to datacollected from boring machinery for mining in an augmented reality viewof a portion of the environment. Boring machinery, and in particularmulti-tip circular boring heads, may experience a range of rockformations at the same time. Sensors may be placed proximal to eachboring tip that may detect forces experienced by the tips. The data maybe collected by a system adapted to collect data in an industrialenvironment and provided to an augmented reality system that may displaythe data as heat maps or the like in a view of the boring machine.

Referring to FIG. 152, an augmented reality display of heat maps basedon data collected in an industrial environment by a system adapted tocollect data in the environment is depicted. An augmented reality viewof an industrial environment 11500 may include heat maps 11502 thatdepict data received from or derived from data received from sensors11504 in the industrial environment. Sensor data may be captured andprocessed by a system adapted for data collection and analysis in anindustrial environment. The data may be converted into a form that issuitable for use in an augmented reality system for displaying heatmaps. The heat maps 11502 may be aligned in the augmented reality viewwith a sensor from which the underlying data was sourced.

In embodiments, methods and systems disclosed herein include anaugmented reality (AR) system in which industrial machine sensed data ispresented in a view of the industrial machine as heat maps of datacollected from sensors in the view. The heat maps are positionedproximal to a sensor capturing the sensed data that is visible in the ARdisplay. In embodiments, the heat maps are based on a comparison of realtime data collected from sensors with an acceptable range of values forthe data. In embodiments, the heat maps are based on trends of senseddata. In embodiments, the heat maps represent a measure of coverage ofsensors in the industrial environment in response to a condition ofinterest that is calculated from data collected by sensors in theindustrial environment. In embodiments, the heat maps of data collectedfrom sensors in the view is based on data collected by a system adaptedto collect data in the industrial environment by routing data from aplurality of sensors to a plurality of data collectors via at least oneof an analog crosspoint switch, a multiplexer, and a hierarchicalmultiplexer. In embodiments, the heat maps present different collecteddata values as different colors. In embodiments, data collected from aplurality of sensors is combined to produce a heat map.

In embodiments, methods and systems disclosed herein include a systemfor data collection in an industrial environment. The system includes anaugmented reality display that presents data being collected from aplurality of sensors in the industrial environment as one of a pluralityof colors. In embodiments, the colors correlate the data being collectedfrom each sensor to a color scale with cool colors mapping to values ofthe data within an acceptable range and hot colors mapping to values ofthe data outside of the acceptable range. The plurality of colorsoverlay a view of the industrial environment and placement of theplurality of colors corresponds to locations in the view of theenvironment at which a sensor is located that is producing thecorresponding sensor data. In embodiments, hot colors representcomponents for which multiple sensors indicate values outside typicalranges. In embodiments, the plurality of colors is based on a comparisonof real time data collected from sensors with an acceptable range ofvalues for the data. In embodiments, the plurality of colors is based ontrends of sensed data. In embodiments, the plurality of colorsrepresents a measure of coverage of sensors in the industrialenvironment in response to a condition of interest that is calculatedfrom data collected by sensors in the industrial environment.

In embodiments, methods and systems disclosed herein include an examplemethod comprising presenting information being collected by sensors inan industrial environment as a heat map overlaying a view of theenvironment so that regions of the environment with sensor datasuggestive of a greater potential of failure are overlaid with a heatmap that is different than regions of the environment with sensor datasuggestive of a lesser potential of failure. In embodiments, the heatmap is based on data currently being sensed. In embodiments, the heatmap is based on data from prior failure data. In embodiments, the heatmap is based on changes in data from an earlier period that suggest anincreased likelihood of machine failure. In embodiments, the heat map isbased on a preventive maintenance plan and a record of preventivemaintenance in the industrial environment. In embodiments, the heat maprepresents an actual failure rate versus a reference failure rate. Inembodiments, the reference failure rate is an industry average failurerate. In embodiments, the reference failure rate is a manufacturer'sfailure rate estimate.

In embodiments, a system for data collection and visualization thereofin an industrial environment may include an augmented reality and/orvirtual reality (AR/VR) display in which data values output by sensorsdisposed in a field of view in the AR/VR display are displayed withvisual attributes that indicate a degree of compliance of the data to anacceptable range or values for the sensed data. In embodiments, thevisual attributes may provide near real-time portrayal of trends of thesensed data and/or of derivatives thereof. In embodiments, the visualattributes may be the actual data being captured, or the derived data,such as a trend of the data and the like.

In embodiments, a system for data collection and visualization thereofin an industrial environment may include an AR/VR display in whichtrends of data values output by sensors disposed in a field of view inthe AR/VR are displayed with visual attributes that indicate a degree ofseverity of the trend. In embodiments, other data or analysis that couldbe displayed may include: data from sensors that exceed an acceptablerange, data from sensors that are part of a smart band selected by theuser, data from sensors that are monitored for triggering a smart bandcollection action, data from sensors that sense an aspect of theenvironment that meets preventive maintenance criteria, such as a PMaction is upcoming soon, a PM action was recently performed or isoverdue for PM. Other data for such AR/VR visualization may include datafrom sensors for which an acceptable range has recently been changed,expanded, narrowed and the like. Other data for such AR/VR visualizationthat may be particularly useful for an operator of an industrial machine(digging, drilling, and the like) may include analysis of data fromsensors, such as for example impact on an operating element (torque,force, strain, and the like).

In embodiments, a system for data collection and visualization thereofin an industrial environment that may include presentation of visualattributes that represent collected data in an AR/VR environment may doso for pumps in a mining application. Mining application pumps mayprovide water and remove liquefied waste from a mining site. Pumpperformance may be monitored by sensors detecting pump motors,regulators, flow meters, and the like. Pump performance monitoring datamay be collected and presented as a set of visual attributes in anaugmented reality display. In an example, pump motor power consumption,efficiency, and the like may be displayed proximal to a pump viewedthrough an augmented reality display.

In embodiments, a system for data collection and visualization thereofin an industrial environment that may include presentation of visualattributes that represent collected data in an AR/VR environment may doso for energy storage in a power generation application. Powergeneration energy storage may be monitored with sensors that capturedata related to storage and use of stored energy. Information such asutilization of individual energy storage cells, energy storage rate(e.g., battery charging and the like), stored energy consumption rate(e.g., KWH being supplied by an energy storage system), storage cellstatus, and the like may be captured and converted into augmentedreality viewable attributes that may be presented in an augmentedreality view of an energy storage system.

In embodiments, a system for data collection and visualization thereofin an industrial environment that may include presentation of visualattributes that represent collected data in an AR/VR environment may doso for feed water systems in a power generation application. Sensors maybe disposed in an industrial environment, such as power generation forcollecting data about feed water systems. Data from those sensors may becaptured and processed by the system for data collection. Results ofthis processing may include trends of the data, such as feed watercooling rates, flow rates, pressure and the like. These trends may bepresented on an augmented reality view of a feed water system byapplying a map of sensors with physical elements visible in the view andthen retrieving data from the mapped sensors. The retrieved data (andderivatives thereof) may be presented in the augmented reality view ofthe feed water system.

Referring to FIG. 153, an augmented reality view 11600 comprising realtime data 11602 overlaying a view of an industrial environment isdepicted. Sensors 11604 in the environment may be recognized by theaugmented reality system, such as by first detecting an industrialmachine, system, or part thereof with which the sensors are associated.Data from the sensors 11604 may be retrieved from a data repository,processed into trends, and presented in the augmented reality view 11600proximal to the sensors from which the data originates.

In embodiments, methods and systems disclosed herein include a systemfor data collection and visualization thereof in an industrialenvironment in which data values output by sensors disposed in a fieldof view in an electronic display are displayed in the electronic displaywith visual attributes that indicate a degree of compliance of the datato an acceptable range or values for the sensed data. In embodiments,the view in the electronic display is a view in an augmented realitydisplay of the industrial environment. In embodiments, the visualattributes are indicative of a trend of the sensed data over timerelative to the acceptable range. In embodiments, the data values aredisposed in the electronic display proximal to the sensors from whichthe data values are output. In embodiments, the visual attributesfurther comprise an indication of a smart band set of sensors associatedwith the sensor from which the data values are outputs.

In embodiments, methods and systems disclosed herein include a systemfor data collection and visualization thereof in an industrialenvironment in which data values output by select sensors disposed in anaugmented reality view of the industrial environment are displayed withvisual attributes that indicate a degree of compliance of the data to anacceptable range or values for the sensed data. In embodiments, thesensors are selected based on a data collection template thatfacilitates configuring sensor data routing resources in the system. Inembodiments, the select sensors are indicated in the template as part ofa group of smart band sensors. In embodiments, the select sensors aresensors that are monitored for triggering a smart band data collectionaction. In embodiments, the select sensors are sensors that sense anaspect of the environment associated with preventive maintenancecriteria. In embodiments, the visual attributes further indicate if theacceptable range has been expanded or narrowed within the past 72 hours.

In embodiments, methods and systems disclosed herein include a systemfor data collection and visualization thereof in an industrialenvironment in which trends of data values output by select sensorsdisposed in a field of view of the industrial environment depicted in anaugmented reality display are displayed with visual attributes thatindicate a degree of severity of the trend. In embodiments, sensors areselected when data from the sensors exceed an acceptable range ofvalues. In embodiments, sensors are selected based on the sensors beingpart of a smart band group of sensors. In embodiments, the visualattributes further indicate a compliance of the trend with an acceptablerange of data values. In embodiments, the system for data collection isadapted to route data from the select sensors to a controller of theaugmented reality display based on a data collection template thatfacilitates configuring routing resources of the system for datacollection. In embodiments, the sensors are selected in response to thesensor data being configured in a smart band data collection template asan indication for triggering a smart band data collection action. Inembodiments, the sensors are selected in response to preventivemaintenance criteria. In embodiments, the preventive maintenancecriteria are selected from the list consisting of a preventivemaintenance action is scheduled, a preventive maintenance action hasbeen completed in the last 72 hours, a preventive maintenance action isoverdue.

FIG. 155 shows a system for data collection in an industrial environmenthaving a self-sufficient data acquisition box for capturing andanalyzing data in an industrial environment including sensor inputs11700, 11702, 11704, 11706 that connect to a data circuit 11708 foranalyzing the sensor inputs, a network communication interface 11712, anetwork control circuit 11710 for sending and receiving informationrelated to the sensor inputs to an external system and a data filtercircuit configured to dynamically adjust what portion of the informationis sent based on instructions received over the network communicationinterface. A variety of sensor inputs X connect to the data circuit Y.The data circuit intercommunicates with a network control circuit, whichis connected to one or more network interfaces. These interfaces mayinclude wired interfaces or wireless interfaces, communicating via astar, multi-hop, peer-to-peer, hub-and-spoke, mesh, ring, hierarchical,daisy-chained, broadcast, or other networking protocol. These interfacesmay be multi-pair as in Ethernet, or single-wire networking protocolsuch as I2C. The networking protocol may interface one or more of avariety of variants of Ethernet and other protocols for real-timecommunication in an industrial network, including Modbus® over TCP,Industrial Ethernet, Ethernet Powerlink, Ethernet/IP, EtherCAT, Sercos®,Profinet™, CAN bus, serial protocols, near-field protocols, as well ashome automation protocols such as ZigBee®, Z-Wave™, or wireless WWAN orWLAN protocols such as LTE™, Wi-Fi, Bluetooth™, or others.

The sensor inputs can be permanently or removably connected to the thingthey are measuring, or may be integrated in a standalone dataacquisition box. The entire system may be integrated into the apparatusthat is being measured, such as a vehicle (e.g., a car, a truck, acommercial vehicle, a tractor, a construction vehicle or other type ofvehicle), a component or item of equipment (e.g., a compressor,agitator, motor, fan, turbine, generator, conveyor, lift, roboticassembly, or any other item as described throughout this disclosure), aninfrastructure element (such as a foundation, a housing, a wall, afloor, a ceiling, a roof, a doorway, a ramp, a stairway, or the like) orother feature or aspect of an industrial environment. The entire systemmay be integrated into a stationary industrial system such as aproduction assembly, static components of an assembly line subject towear and stress (such as rail guides), or motive elements such asrobotics, linear actuators, gearboxes, and vibrators.

FIG. 156 shows an airborne drone 11730 data acquisition box with onboardsensors 11732 and four motors 11734 to provide lift and movementcontrol. In embodiments, the drone 11730 has a charging dock capabilityand in embodiments, a battery changing capability so that the same drone11730 can return to inspection after a brief return to base for batteryreplacement. The drone 11730 can travel from a location near the systemsto be sensed. The drone 11730 can detect the presence of other sensordrones and avoid collisions based on both active sensors andnetwork-coordinated flight plans. These sensor drones 11730 inspect andsense environmental and apparatus conditions based on scheduled tours ofsensor reconnaissance. They also respond to specific events, eithercommand driven (human requests for additional data), requests from otherdrones, events such as a detected anomaly in an item to be sensed withmore scrutiny e.g., sensing by multiple drone s with multiple sensors.They respond to AI both integrated into the drone 11730 or located in aremote server, that analyzes conditions and generates a request foradditional data and inspection of an environment or apparatus. The drone11730 can be configured with multiple sensors. For instance, most drones11730 are equipped with some sort of visual sensor, either in visuallight or infrared range, as well as certain forms of active guidancesensor technology such as light-pulse distance sensing, sonar-pulsesensing. In addition, drones 11730 can be equipped with additionalsensors such as specific chemical sensors and magnetic sensors designedto analyze the materials of specific apparatus and machinery.

FIG. 157 shows an autonomous drone 11780 with multiple modes ofmobility, optionally including flight, rolling and walking modes ofmobility. In embodiments, telescoping and articulating robotic legsallow positioning on uneven surfaces. In embodiments, the drone may havefour wheels. The various mobile platforms may include articulating legsthat can pull up and away to allow rolling on wheels on smooth surfaces.The legs may include end members (e.g., “feet”) that may be enabled withvarious forms of attachment by which the drone may attach to an elementof its environment, such as a landing spot on a piece of industrialequipment proximal to a point of sensing (e.g., near a set of bearingsof a rotating component). The end members may be enabled with variousforms of attachment, such as magnetic attachment, suction cups,adhesives, or the like. In embodiments, the drone may have multipleforms that can be engaged by alternative mechanisms on end members(e.g., rotating between elements with different attachment types) orthat can be retrieved by the articulating legs from a storage locationon the drone. In embodiments, the drone 11780 may have a robotic arm11782 that has the ability to place an adhesive-backed hook and loopfastener element onto a machine to allow attachment, disengagement andreattachment by the drone at a desired landing point. Placement may beundertaken under control of a vision system, which may include aremote-control vision or other sensing system and/or an automatedlanding system that recognizes a type of landing point andautomatically, optionally with pattern recognition and machine learning,can land the drone and initiate attachment. Placement may be based bothon the recognition (including by machine vision or sensor-basedrecognition) of an appropriate sensing location (such as based on anidentified need for sensing, a trigger or input, or the like) and of anappropriate landing position (such as where the drone can establish astable attachment and reach the point of sensing, such as with anarticulating robotic arm). In embodiments, a camera system and othersensors can detect surface geometry and characteristics to selectappropriate landing and engagement modes (e.g., a rough verticalsurface, if recognized, can trigger use of legs and articulated fingersto hold on, while a smooth vertical surface, if recognized, can triggeruse of suction cups or magnets to establish temporary attachment).

In embodiments, machine learning can vary and select landing andengagement modes by variation and selection, including testing securityof various forms of attachment. Machine learning can be, or be initiatedusing, a set of rules for landing and engagement, a set of models (whichmay be populated with information about machines, infrastructureelements and other features of an industrial environment), a trainingset (including one created by having human operators land a set ofdrones and engage with sensors), or by deep learning approach fusingvarious visions and other sensors through a large set of trial landingand engagement events.

In embodiments, a camera 11788 may have object recognition capabilities(including pattern recognition improved by machine learning, rule-basedpattern matching to library of images of machines and other features, ora hybrid or combination of techniques).

In embodiments, sensor-based recognition of industrial machines may beprovided, where a machine is recognized based on sensor signatures(e.g., based on matching to known vibration patterns, heat signatures,sounds, and the like that characterize generators, turbomachines,compressors, pumps, motors, etc.). This may occur based on rules,models, or the like, with machine learning (including deep learning orlearning based on human-generated training sets), or variouscombinations of these.

In embodiments, the mobile platforms may contain one or moremulti-sensor data collectors (MDC) 11790 may be disposed on one or morearticulating robotic arms 11782, which may move from the interior to theexterior of the drone 11730. In embodiments, the drone may have one ormore of its own articulating robotic arm(s) 11782, such as for pickingup and placing individual sensors, attaching sensors to a point ofsensing, attaching sensors to power sources, reading sensors, or thelike.

In embodiments, the MDC 11790 can swap in and out various sensors, bothat the point of sensing and by interacting with a central station 11792,where the drone 11730 can replenish the MDC 11790 with new or differentsensors, can re-stock any disposable or consumable elements (such astest strips, biological sensors, or the like) or the like. Replenishmentand re-stocking can be undertaken with control elements describedthroughout this disclosure that involve selection of sensor sets,including rule-based, model-based, and machine learning control withinan expert system.

In embodiments, the drone 11730 can be paired with the central station11792, such as for wireless re-charging, re-stocking of sensors, securefile downloads (e.g., requiring physical connection and verification),or the like. The central station 11792 may have network communicationwith a remote operator (including an expert system) and/or with localoperators, such as via one or more applications, such as mobileapplications, for controlling elements of the drone 11730 or the centralstation 11792 or for reporting or otherwise using information collectedby the drone 11730 or the central station 11792.

In embodiments, the central station 11792 can have a 3D printer, such asfor printing suitable connectors for interfacing with machines, forprinting disposable or consumable elements used in sensors, for printingelements such as end members for assisting with landing, and the like.

In embodiments, the MDC 11790 has interface ports for various forms ofinterface, including physical interfaces (e.g., USB ports, firewireports, lighting ports, and the like) and wireless interfaces (e.g.,Bluetooth, Bluetooth Low Energy, NFC, WiFi and the like).

In embodiments, the MDC 11790 interfaces can include electrical probes,such as for detecting voltages and currents, such as for detecting andprocessing operating signatures of electrical components of anindustrial machine.

In embodiments, the MDC 11790 carries or accesses (such as within thedrone 11730, or the central station 11792) various connectors to allowit to interface with a wide variety of machines and equipment.

In embodiments, the camera 11788 can identify a suitable interface portfor an industrial machine and select and under user remote control orautomatically (optionally under control of an expert system disposed onthe drone 11730 or located remotely) use the appropriate connector forthe interface port, such as to establish data communication (e.g., withan onboard diagnostic or other instrumentation system), to establish apower connection, or the like.

In embodiments, the robotic arm 11782 of the MDC 11790 can insert one ormore cables or connectors as needed, such as ones retrieved from storageof the drone 11730 or from a central station. The central station canprint a new connector interface as needed.

In embodiments, the drone 11730 is self-organizing and can be part of aself-organizing swarm that includes intelligent collective routing ofseveral drones 11730 for data collection. The drone 11730 can have andinteract with a secure physical interface for data collection, such asone that requires local presence in order to get access to controlfeatures.

The drone 11730 may use wireless communication, including by acognitive, ad hoc mobile network of a mesh network of the drones 11730,which mesh network may also include other devices, such as a mastercontroller (e.g., a mobile device with human interface).

In embodiments, the drone 11730 has a touch screen display for userinteraction and mobile application interaction.

In embodiments, the drone 11730 can use the MDC 11790 to collect datathat is relevant to placement of sensors for instrumentation of machines(e.g., collect vibration data from a set of possible locations andselect a preferred location for data collection, then dispose asemi-permanent vibration sensor there for future data gathering).

Intelligent routing can include machine-based mapping, includingreferencing a pre-existing map or blueprint of an industrial environmentand using machine learning to update the map based on detectedconditions (e.g., detecting by camera, IR, sonar, LIDAR, etc., thepresence of features, machines, obstacles, or the like, whether fixed ortransient and updating the map and any relevant routes to reflectchanging features).

In embodiments, the drone 11730 may include a facility for sensor-baseddetection of biological signatures (e.g., IR-sensing for base-levelrecognition of presence of humans, such as for safety), as well as otherphysiological sensors, such as for identity (e.g., using biometricauthentication of a human before permitting access to collected data orcontrol functions) and human status conditions (such as determininghealth status, alertness or other conditions of humans in theenvironment). In embodiments, the drone 11730 may store or handleemergency first aid items, such as for delivery to a point of emergencyin case that an emergency health status is determined.

In embodiments, the drone 11730 can have collision detection andavoidance (LIDAR; IR, etc.), such as to avoid collisions with otherdrones 11730, equipment, infrastructure, or human workers.

In another embodiment, the system in FIG. 157 is informed, based on ascheduled event, to evaluate the condition of various aspects of afactory floor. The system, configured with a learning algorithm, takessamples of various sensors in various positions. It is provided withpositive reinforcement of a correctly operating factory floor on aregular basis. When there is a fault, it will be instructed to evaluatethe condition of various aspects and taught that there is a fault. Itrecords the sensor data such as temperature, speed of motion, positionsensors. It also integrates additional sensor data such as data fromsensors that are integrated into the system to be analyzed, such asposition, temperature, and structural integrity sensors integrated in arail guide in an assembly line. These sensors communicate sensor dataincluding real-time and historical sensor data to the system via a oneof the network communication interfaces.

In another embodiment, the system in FIG. 157 has a robotic arm andcarries with it numerous attachable modules each of which providessensing of a different type of signal or data. For instance, the systemmay carry with it four modules, capable of sensing temperature, magneticwaves, lubricant contamination, and rust. It is capable of attaching anddetaching and securely storing each type of module. The mobile drone11730 is capable of returning to a charging station and selectingadditional modules to measure additional types of signal. For instance,the system may receive an indication that a portion of a factory has afault in the area where a vibrator is designed to shake tiny componentsinto hopper which pours into a conveyer belt, which feeds into apick-and-place robotic arm comprising gear boxes and actuators. Thesystem, having received an indication that there is a failure mode suchas a slowdown or jam in this general area, retrieves a chemical analysismodule and tests the viscosity and chemical condition of the lubricantin the mechanical vibrator. It then retrieves a different chemicalanalysis module to analyze a different type of lubricant used in thegear box and actuator of the robotic arm. It then, delivering the dataover a network interface and receiving an indication to continuetesting, retrieves a new module capable of detecting mechanical faultsas well as a visual camera module. Having retrieved these modules, thesystem then performs a visual analysis of the parts of the assembly lineand sends them to a remote server (or keeps them locally) to be comparedwith historical pictures of the same portion of assembly line. Thesystem continues in this way until all of the sensors which an externalsystem has specified (such as a manually controlling human or apredetermined list) and have been completed, or until one of the sensorsdetects an anomaly which is quantified and communicated to an externalsystem to propose a repair.

FIG. 158 shows a drone data acquisition system which is movably attachedto a track and which can, through translational motion and repositioningof a sensor arm, position itself in proximity to a portion of a systemto be sensed and diagnosed for failure modes. The robotic arm 11782 iscapable of positioning, for instance, a highly sensitive metallurgicalfault detection system such as an x-ray or gamma-ray radiograph or anon-destructive scanning electron microscope. The robotic arm 11782positions its sensing arm and measurement device in various positions ona static or dynamically moving target such as a set of rolling bearingsin an assembly line. The robotic arm 11782 of the system performshigh-resolution image capture and failure mode detection on thestructural aspects of the roller bearings such as detecting if there areany roller bearing failure modes such as pitting, bruising, grooving,etching, corrosion, etc. The system then communicates the findings ofthe failure mode detection to a remote system over a network interface.

In another embodiment, the data acquisition system of FIG. 158continually performs a predetermined set of measurements over time andcompares these measurements over time. For instance, it can measure thedecibels of sound received at a precisely positioned directional soundinput sensor aimed at each of a set of roller bearings over time. When,after some time a roller bearing diverges from the usual or common orspecified decibel range for audio, the failure mode of that specificroller bearing is indicated, and the system then communicates thefindings of the failure mode detection to a remote system over a networkinterface.

FIG. 159 shows a stationary guide rail 11800 in an industrialenvironment, and below it, a pair of ports 11802 including a networkinterface jack and a power port jack. A mobile data acquisition systemsuch as a flying drone 11730 or wheeled sensor robot approaches theguide rail and uses a moving extension to “jack in” to the ports. Atthis point, the system can continue to operate indefinitely because itis in network communication and has continuous power. In embodiments, aremote operating user can now activate any of the sensors available tothe mobile system and direct them to any reachable portion of thetarget, including the rail guide and any machinery moving on the guide.The rail guide can be chemically inspected. The rail guide can bevisually inspected. The portion of the assembly line in which the railguide operates can be visually monitored by the remote user operatingthrough the system sensor. The system can perform auditory testing ofthe machinery operating and moving along the rail guide. Any sensorsembedded in the rail guide can communicate their sensor data to theattached roving system. Similarly, the sensor input from the attachedroving system can be integrated with any embedded sensor data from therail guide and delivered together with it over the wired networkinterface. Any drone 11730 connected to hover in proximity to the railguide and its associated functionality can operate indefinitely andprovide “zoomed in” monitoring of that portion of the assembly line. Ifa portion of an assembly line indicated a fault, a group of drones andwheeled data acquisition systems can be recruited to more closelymonitor that area. In the case of a remote human operator, thisadditional sensor visibility affords them numerous real-time streams ofsensor information on various aspects of the portion of the assemblyline. The remote human operator can reposition and change the sensingmodes of the various data acquisition systems. In another embodiment, aremote machine learning system operates the multiple sensing systems tozoom in and acquire additional data about the area of the assembly linethat has been detected to be at fault. Through iterative trials andfeedback, the machine learning system operates the data acquisitionsystems to test different signals with different sensors in differentpositions until one or more failure modes have been positivelydiagnosed. The machine learning system then takes appropriate actionsuch as disabling that section of the assembly line to prevent loss ofvalue from further damage, communicating to an on-site operator what thediagnosed fault was, automatically ordering the correct parts fordelivery and creating a trouble ticket in a repair system, automaticallycalling a service technician to go to the location and repair the fault,estimating the total predicted downtime and automatically updating anaccounting system with the modified throughput based on when the systemwill be producing again.

FIG. 160 shows a portion of the drive train 11810 and chassis of avehicle 11812 such as a car or truck for transportation or an industrialvehicle such as a tractor for use in construction or farming. Itconsists of an engine 11814 a transmission 11818, a propeller shaft11820, a rear differential gear box 11822, axles, and wheel ends. Thevarious sensor drones disclosed herein can sense, monitor, analyze andre-monitor the vehicle 11812. The sensor drone 11730 may be airborneduring its data recording. A sensor drone 11840 may be connected to thevehicle during the entire assembly process or at certain stations in theprocess. FIG. 163 shows a portion of a turbine 11900. The various sensordrones disclosed herein can sense, monitor, analyze and re-monitor theturbine 11900. The sensor drone 11730 may be airborne during its datarecording. The sensor drone 11840 may be connected to the vehicle duringthe entire assembly process or at certain stations in the process. Thesevarious components are metallic and are subject to wear and damage fromoveruse and underuse outside their duty cycle and working output range.In order to operate this equipment and maintain these various componentsin proper order, numerous sensors are disposed throughout the equipmentand the various components. Conventionally, the most active elementssuch as the transmission contain numerous sensors which are used tooperate the device correctly and provide feedback, but not necessarilyto diagnose or monitor the health or failure modes of the device. Thesesensors include throttle position sensors, mass air flow sensors, brakesensors various pressure and temperature, and fluid level sensors. Thesesame sensors, along with numerous other additional sensors, can be usednot only for operation but for maintenance and diagnosis of the device.Additional sensors, which can be permanently installed and distributedthroughout, include lubricant pollution chemical sensors such assolid-state sensors, gear position sensors, pressure sensors, fluid leaksensors, rotational sensors, bearing sensors, wheel tread sensors,visual sensors, audio sensors, and numerous other sensors listed herein.

FIG. 161 shows a micro, mobile magnetically driven attachable dronesensor system 11840 that attaches to metal and can be used to performanalysis of a vehicle in motion or at rest.

It consists of a small rectangular or square mobile sensor unit whichcan be sized smaller than a matchbox. It has numerous wheels or castorsor ball bearings and it attaches to metal using a permanent orelectromagnet. It can be curved to mate more easily to curved surfacessuch as a rear differential or drive or propeller shaft.

FIG. 162 shows a closer view of the mobile sensor system, showing itswheels and four sensors, an ultrasonic sensor, a chemical sensor, amagnetic sensor and a visual (camera) sensor. The system travels aroundand throughout the target area for failure mode detection, such as theundercarriage of a transportation or industrial vehicle. The sensorcaptures comprehensive data and is capable of covering the entiresurface and undercarriage of the vehicle and can detect faults such asrusted out components, chemical changes, fluid leaks, lubricant leaks,foreign contamination, acids, soil and dirt, damaged seals, and thelike. The sensor system reports this information over a networkinterface to another sensor, to a computer on the vehicle itself, or toa remote system in order to facilitate data capture and ensure that thedata is fully recorded. The system also runs on a periodic basisperforming the same or similar coverage of the vehicle so that abaseline measurement can be compared with later measurements todetermine the state of maintenance of the vehicle. This can be used todetect failure modes but can also be used to create an image of thevehicle for insurance, for depreciation, for maintenance scheduling, orsurveillance purposes.

In embodiments, the mobile attaching drone sensor 11840 can be removablyattached to a portion of a vehicle and can move freely around theundercarriage of a vehicle. It can also be placed there as a sensingmodule by the mobile robotic sensor system of FIG. 157 and subsequentlyretrieved when it has completed its sensing tasks.

In embodiments, the mobile attaching sensor 11840 may take the form of aswimming device that can travel through fluid, or a multi-pedal unitwith chemically-adhesive or magnetic or vacuum-adhesive pods or feetthat allow it to move freely on the surface of a target to be sensed.

In embodiments, the modular sensors shown in FIG. 157 can be removablyor permanently integrated into mobile or portable sensors such asdrones, multi-pedal or wheeled industrial measurement robots, orself-propelled floating, climbing, swimming, or magnetically crawlingmicro-data acquisition systems. Any of the sensors can take multiplemeasurements from different positions on the same target to get a fullerpicture of the health or condition of the target.

The sensors deployed on the various drones, mobile platforms, robots,and the like may take numerous forms. For instance, a set of rollerbearing sensors may be integrated within the roller bearing itself,using the energy off the motion of the roller bearing to generate aninductive force sufficient to generate data signals to communicate to adata circuit the state of the roller bearing, such as velocity,rotations per unit time, as well as analog data indicating any minorperturbations in the smooth rotation of the bearing over time. Adeformation sensor can take the form of a passive (visual, infrared) oractive scanning (Lidar, sonar) system that captures data from a targetand compares it to historical data on the shape or orientation of thecomponent to detect variations. Camera sensors are configured with alens to capture continuous and still visible and invisible photoninformation cast upon or reflected by a target. Ultraviolet sensors cansimilarly capture continuous and still frame information about a targetand its surroundings. Infrared sensors can capture light and heatemission data from a target. Audio sensors such as directional andomnichannel microphones can measure the frequency and amplitude of sonicwave data emitting from a target or its environment, and this data canbe compared over time to detect anomalies when the amplitude or qualityof the sound generated by the target exceeds or varies frompredetermined or historical levels. Vibration sensors can be used in asimilar manner, capturing extremely low frequency sound as well asphysical perturbations and rhythms of a target over time. Viscositysensors can be installed in-line in the lubrication system of a systemor vehicle or can be movable and make ad-hoc measurements andevaluations of the continuous or instantaneous viscosity of thelubricating material for a target. Chemical sensors can vary widely inwhat analyte (target chemical) they detect, and in the case of vehiclesor stationary machinery, can be configured with variable receptorscapable of capturing and recognizing numerous conditions of a target.Specific target sensors such as rust sensors or overheat sensors cansense when a target such as an apparatus, metal structure or chemicallubricant has started to change chemically over time. These chemicalsensors can be multi- or single-purpose, and can be integrated within astructure, such as the frame or chassis of a vehicle or the stationaryor movable portions of an assembly line. These chemical sensors can beintegrated into components providing the mechanical motive power of anengine or robotic machinery. These chemical sensors can be attached to aportable self-propelled data acquisition system that is deployed tomeasure the target. When activated these chemical sensors make contactor take samples from the target and perform chemical analysis and reportthe state of the results to a data circuit. A solid chemical sensor cantake solid chemical samples (rather than gaseous or liquid samples) anddetermine the presence of a particular chemical or the composition bydetecting multiple chemicals in a sample. A pH sensor can be used todetect the level of acidity of a target and can be used to determinespecific changes in the environment of a target, the fluid conditionssurrounding a target, or the state of an operational fluid such as acoolant or lubricant in a target, and similarly, fluid, and gaseouschemical sensors perform additional component and presence detection onthese targets. A lubricant sensor can be as simple as an indicator ofwhether sufficient lubricant is still present (by detecting chafing or alack of distance between conductive or hard components) or can use acombination of chemical, pressure, visual, olfactory, or vibrationalfeedback tests (vibrating the target and measuring response) todetermine the instant or continuous presence or quantity of lubricant ina target. Contaminant sensors can look for the presence of foreign ordamaged elements added to the surface, substance or fluid contents of atarget, such as a lubricant which has been contaminated with metalparticles from component wear, or when a lubricant or motive fluid suchas in a pneumatic system has been contaminated due to the breaking of aseal. Particulate sensors can detect the presence of specific types ofparticles within a fluid or on a target. Weight or mass sensors candetermine the continuous or changing weight of a component, and can beon coarse scale such as a weighing device for weighing large machinerydown to an integrated MEMS scale that determines the continuous andinstantaneous changes in weight of a target that may lose mass over timedue to damage or abrasion or evaporation, sublimation, etc. A rotationsensor can be optical, audio-based, or use numerous other techniques todetect the periodic acceleration, velocity, and frequency of rotation ofa target. Temperature sensors can be configured to measure coarseenvironmental temperature in a general area as well as fineenvironmental temperatures, precise temperature of a region of a targetcomponent and can be disposed throughout an engine, a robotic system, orany stationary or moving component. Temperature sensors can also bemobile and deployed to take periodic or ad-hoc measurements of a targetcomponent, surface, material, or system to determine if it is operatingin a correct temperature range. Position sensors can be as simple asinterrupted visual reflections, to visual systems with image-recognitionalgorithms being performed on continuous video, to magnetic ormechanical switch systems that durably detect either precisely orcoarsely the position of various moveable elements with respect to oneanother. Ultrasonic sensors can be used for a variety of distance,shape, solidity, and orientation measurements by projecting ultrasonicenergy in the direction of a target or group of targets or measuring thereflected ultrasonic energy reflected by those targets. Ultrasonicsensors may comprise multiple emitters and receivers in order to adddimensions and precision to the measurements and even produce 2D or 3Doutlines of a region for further analysis. A radiation sensor can detectthe presence of forms of radioactivity as alpha, beta, gamma, or x-rayradiation and some can identify the directional source, the field andarea of the radiation and the intensity. An x-ray radiograph canactively determine structure, structural changes and structural defectsas well as providing a visual depiction of otherwise obscured physicalcharacteristics of a target. Similarly, a gamma-ray radiograph can beused to penetrate solid targets such as steel or other metallic objectsand so determine the characteristics of physical features such asjoints, welds, depths, rough edges, and thicknesses in load bearing andpressurized targets. Various forms of high-resolution scanningtechnologies exist including scanning tunneling microscopes, photontunneling microscope, scanning probe microscopes, and these measurementdevices have been miniaturized and non-destructive forms of thesedevices can be brought in contact with a target to be measured, such asvia the movable robot or drone 11730, and then used to perform extremelyhigh resolution (atomic-scale) measurements and analysis of thestructure and characteristics of a target. A displacement meter can beimplemented using capacitive effects, mechanical measurement or lasermeasurement and can be used similarly to a position meter to measure thelocation of a movable target and can be used, for instance, to measurethe ‘play’ or changing displacement of a wearing physical target overtime. A magnetic particle inspector can be used to determine if a fluidsuch as a lubricant, an immersive fluid container, a coolant, or apneumatic fluid, for instance, contain trace elements of ferromagneticparticles, which could be an indication of the decay or failure of ametal component. An ultraviolet particle detector can be used to detectcontamination such as in gaseous targets. A load sensor such as a staticload sensor (measuring systems at rest) or an axial load sensor thatdetects, such as magnetically, the pushing and pulling forces along abeam and can be used to determine the forces on an axle or othertorque-transmitting tube or shaft. An accelerometer can be microscopicin size, implemented as a MEMS device, or packaged as a largerindustrial device and can provide multiple dimensions of accelerationand gravitation data about or in proximity to a target, and can beuseful for instance to detect if a device is level, or in addition toother data collection, the amount of force being applied to a targetover time. A speed sensor can be used to measure translational,displacement or rotational velocity or speed. A rotational sensor can beused to measure the speed, period, frequency, even or uneven motion of arotating element such as a tire, a gear, an armature, or a gyro. Amoisture sensing device can detect the liquid, condensation or H2Ocontent of the target or its environment. A humidity sensor can measurethe degree of water vapor in the atmosphere in the vicinity of a target.Ammeters, voltmeters, flux meters, and electric field detectors can beused to measure electromagnetic effects, fields and levels of a targetor in the vicinity of a target, or the electronic or magnetic emissionof a target, or the potential energy stored in a target. A gear boxsensor can measure numerous attributes of an industrial gear box forgeneral translation of motive power in a robotic or assembly lineenvironment as well as numerous complex vehicular gear assembliesincluding vehicle transmissions and differentials. Measurements caninclude the precise position of all internal gears, the state of wear ofgear elements and teeth, various chemical, temperature, pressure,contamination, coolant level, fluid level, vacuum level, seal level,torsion, torque, force, shear stress, cycle count, tooth gap, wear, andany other changing physical attribute. A gear wear sensor and “toothdecay” sensor can specifically measure and convey the degree to whichgears have worn down or that the teeth of the gears have been chipped,cracked, flaked off or otherwise reduced from original condition, andthis can be accomplished through visual or other emitting signalsensors, audio sensors (measuring change in sonic quality based on thechange in impact of teeth), laser sensors (measuring the periodicinterruption of a precise beam across each gear path), powertransmission measurement (measuring loss of power from one gear to thenext via torque or force measurement) and numerous other techniques. Atransmission input speed sensor measures the rotational velocity of theshaft entering the transmission and can do this with rotational positionsensors plotted against time. Transmission output speed sensors measurethe rotational velocity of the shaft delivering motive force out of thetransmission. A manifold airflow sensor or mass air flow sensor can beused to measure the air density or intake airflow of an engine and thusdetermine the amount of engine load, torque, or power output. Othertypes of engine load sensors can be used to determine how much power ortorque is being delivered from an engine, such as by measuring thedelivered axle speed vs. the expected axle speed or by measuring thework being produced. A throttle position sensor measures the position ofan engine throttle regulating the amount of fuel and air entering anengine, and can be measured using various techniques such as hall effectsensing, inductive, mechanical position sensing, magneto resistivesensing, and other techniques. A coolant temperature sensor measures thecoolant temperature in various positions, over time or instantaneouslyin a liquid or gas cooled target system. A speed sensor can measurerotational or linear speed or speed of an overall vehicle over a path ora moving part in rotational or translational motion. A brake sensor canmeasure various aspects of a vehicular or robotic braking system thedegree to which a brake activation switch (such as a vehicular brakepedal) is depressed, or the degree to which a brake is activated or thedegree to which a brake is making frictional or other speed-suppressingcontact with the motion system. A fluid temperature sensor can measurethe temperature of any fluid such as a gaseous, pressurized, lubricant,cooling, fuel, or transported substance and can measure it in a singlelocation or in various locations throughout the body of the fluid, andsuch measurements can be achieved through integrated contact sensors,dispersed contact sensors around the perimeter of a container, orthrough active or passive measurement such as infrared sensing ormeasuring the effect of applied energy to a portion of a fluid and thereflected or measured effect, such as with a laser thermometer. Anemitting thermometer tool can be directed to various portions of athree-dimensional fluid chamber to be measured. A tool load sensor canbe used to determine the amount of power being delivered from a tool andthe resistance of the moving parts against the expected unloaded powerof that device. A bearing sensor can measure the forces in portions orthroughout or at periodic intervals in a bearing and thus allow a systemto measure the change in these forces over time, as well as measureother aspects of a mechanical bearing such as position, service life,rotational count, change in average velocity, sonic changes, vibrationalchanges, chemical changes, color changes, surface changes, contaminationchanges, and numerous other attributes relevant to change of the bearingand its potential performance over time. A standstill counter canmeasure when and how often and for how long and how rapidly a movabletarget is stationary and in what internal position (as in a rotationalor movable element) or relative position (as in a device that interfaceswith another device) the moveable target is holding still, which canamongst other things indicate a location where a device, by sitting inthat specific position may develop a fault or unwanted physicalasymmetry. A hydraulic pump or power unit sensor can sense the pressurewithin the hydraulic fluid that provides power and also help detect,based on non-linearity or other specific signals that the hydraulicfluid is aged, compromised, contaminated, oxygenated or otherwise atfault. Hydraulic pump and power unit sensors can also sense otheraspects of a pump or power unit including service duration,displacement, current position, divergence from duty cycle, change inrange of motion or velocity curve of motion over time, resistance, fluidtemperatures and chemical state of the fluid enclosure, enclosureintegrity, and other intrinsic aspects of the pump. An oxygen sensor cansense the presence, quantity, or density of oxygen in the environment orin a target container. Gas sensors can detect specific types of gascompositions using either a consumable chemical reagent or a solid-statechemical sensor and can detect the presence, quantity or density of aparticular gas or combination of gasses in an environment or targetcontainer. Oil sensors can detect the presence of oil, its viscosity,its level of pollution, and its pressure in a target area or container.A chemical analysis sensor can use consumable or permanent sensors toanalyze a sample and determine the presence of a single chemicalmolecule or element or the composition of a sample and the specificmultiple chemicals that make it up and their relative quantities.Chemical analysis sensors use various techniques including spectralanalysis, exposure to lights, combination with consumable test strips,solid-state chemical sensors, and other techniques to establish thechemical makeup of a target. Pressure detectors can detect the pressurein an environment (such as barometric pressure) or can be movably linkedto an openable shaft such as with an inflatable object or tire with atire stem or a pneumatic device or a gas-filled device such as arefrigerant unit, and can measure the pressure therein. Pressuredetectors can also be permanently installed within a compressed orvacuum chamber and communicate their measurements through a wired orwireless channel. A vacuum detector can measure the level the relativestate of pressure of the interior and can also produce a result simplyindicative of whether a predetermined level of vacuum exists in achamber. A densitometer can measure the optical density e.g., degree ofdarkness of a sample, by projecting one or more forms of light on it andmeasuring absorption. A torque sensor can measure the dynamic or statictorque of a rotating element using techniques such as magneto elasticsensing, strain gauges, or surface acoustic waves. Engine sensors canmeasure numerous aspects of an engine, including pressures,temperatures, relative positions, velocities, accelerations, fluiddynamics, power transfer, and numerous other states in a vehicle orother power-generating engine. Exhaust and exhaust gas sensors canmeasure the output of an exhaust system for attributes such as relativechemical composition, presence of specific chemicals, pressure,velocity, quantity of specific particles, particle count, and quantityof specific pollutants. Exhaust sensors can be disposed within the oneor more pipes or channels through which exhaust exits, and can becomposed of numerous different sensors including catalytic sensors,optical sensors, mechanical and chemical sensors that analyze theexhaust. A crankshaft sensor or crankshaft position sensor can useoptical, magnetic, electrical, electromechanical, or other techniques toestablish and report the real-time velocity of a crankshaft or itsposition relative to other components including the specific position ofthe pistons in a reciprocating motor. A camshaft position sensor can useoptical, magnetic, electrical, electromechanical, or other techniques toestablish the position of the camshaft and can feed this back toignition and fuel delivery systems in a feedback loop as well as providethe information to an external system for analysis. A capacitivepressure sensor uses capacitive electrical effects to measure thepressure inside a target chamber. A piezo-resistive sensor can be usedto measure strain and distortion of surfaces and devices under load. Awireless sensor can encompass a wide range of different sensing unitsthat deliver the information they sense over a wireless connection. Awireless pressure sensor performs pressure sensing and delivers theresults over a wireless connection. A fuel sensor can use pressure,optical sensing, mechanical sensing with a float, weight, ordisplacement sensing to determine the level of fuel within a tank, andother types of fuel sensors can sense fuel flow as it passes through achannel or into a chamber. A gyro sensor can measure angular orrotational velocity and can produce signals useful for physicalstabilization and motion sensing. Mechanical position sensors measurephysical displacement, angular displacement, relative position ororientation using mechanical, optical, magnetic, electrical, or othersensing techniques. MEMS (Micro-electrical-mechanical) aremicrofabricated sensors which can be integrated into objects to bemeasured or integrated in mobile sensing devices and MEMS sensorsencompass various sensing devices including pressure sensors, magneticfield sensing, accelerometers, fluid quantity sensors, microscanningsensors, micromirror steering devices for sensing, ultrasoundtransducing, as well as MEMS devices that harvest energy which can beused to power the transmission of sensor data. An injector sensor maysense characteristics of a fuel delivery such as the quantity, speed, ortiming of fuel injection. A NOx sensor detects the pollutant nitrogenoxide such as in exhaust systems. A variable valve timing sensor can beused in feedback systems to verify and help control the timing of valvelifting in an engine equipped with variable valve control for fuelefficiency and performance optimization. A tank pressure sensor candetect evaporative leaks in a gasoline or diesel fuel tank due to anabsent gas cap, and in other tank applications such as pressurized tankscan detect how full a gaseous tank is. A fuel flow sensor is aspecialized fluid flow sensor, both of which can measure the quantity ofa gas or liquid passing through a region in a unit time, such as wateror fuel or gasses in a pipe or flue. An oil pressure sensor can belocated in various places in an engine, transmission, gearbox, or othersealed lubricating system to help determine the performance andsufficiency of the lubricant. A damper sensor or throttle positionsensor measures the position of a partial valve system and can measurethe degree of flow permitted in an intake, exhaust and other flow damperor throttle engine or industrial system. A particulate sensor orparticulate matter sensor can detect specific air quality conditionssuch as the presence of particulates and dust. An air temperature sensorcan be located in various portions of an engine to receive data that canhelp optimize the air/fuel mixture in an engine. A coolant temperaturesensor can sense the temperature of coolant passing through an area orstored in a chamber and help determine if a cooling system is operatingas intended. An in-cylinder pressure sensor can capture data about theinstantaneous pressure in a motor cylinder and so optimize thecombustion in an engine. An engine speed sensor can sense the rotationalmotion of the crankshaft using optical or magneto-electric sensing. Aknock sensor uses vibration sensing to measure the magnitude and timingof detonation in an engine and can be used to adjust the ignitiontiming. A drive shaft sensor can measure numerous aspects of apower-delivering shaft including angular velocity, power transfer, andmay incorporate specific sensors for various modes of vibration such asa torsional vibration sensor, a transverse vibration sensor, a criticalspeed vibration sensor which detects vibration at the natural frequencyof the object leading to failure modes, and a component failurevibration sensor which can detect failure modes in u-joints or bolts. Anangular sensor can measure the angular position of a mechanical bodywith respect to a reference point. A powertrain sensor encompassesvarious sensors throughout theengine-transmission-driveshaft-differential-wheel system. An enginesensor can include a power sensor encompassing various sensors thatdetect the level of power being delivered by the engine. Engine oilsensors can sense oil pressure, temperature, viscosity, and flow. A loadsensor can sense weight or strain in a static configuration. A frequencysensor can measure various frequencies or provide positive confirmationthat a signal or input is maintaining a particular frequency. A transfercase sensor in four-wheel or all-wheel drive vehicles can detect theposition of the gears (high or low). A differential sensor such as arear wheel speed sensor indicates the axle speeds of the rear wheels,such as for an anti-lock braking system. Various other sensors in therear differential can detect conditions such as lubricant sufficiency,seal, power transfer, slip, etc., A tire pressure gauge is a specializedform of pressure gauge and can be integrated with a hub or rim in thevalve stem or can be non-integrated and connected to the valve stem asneeded. A tire damage gauge can sense pressure loss, traction loss, orusing other sensor techniques determine various attributes of a tiresuch as wear, tear, balding, splitting, puncture, and the like. A tirevibration or balance sensor can sense when a wheel is not smoothlyrotating. Hub and rim integrity sensors can measure and detect thestructural integrity and stability of wheels through chemical,electromagnetic, optical, or visual sensing. Air, fluid and lubricantleak sensors can detect the loss of air or fluid through various meansincluding pressure change over time, visual detection of a puncture,emission of gas or liquid from the exterior of the containing vessel, ortemperature gradient detection such as with infrared sensing. Lubricantleak sensors can also detect a loss of lubricant through increased noisedue to abrasion, fine measures of distances and contacts between parts,vibrations, and off-balance motions in a system.

The sensors described herein can deliver their instantaneous orcontinuous sensor data via numerous data transmission techniques,including techniques such as low-distance wireless transmission wherethe power to emit the transmission is provided by an inductive ormechanical generator which is powered by the motion or energy beingsensed. The sensor data can be delivered via a single wire or evenbody-current transmission protocol over any practical energy emissiondevice. For instance, a pressure sensor embedded within a ferrometallicblock could use the fluctuations in temperature to induce a tinymagnetic flux in the block, which flux is then measured in another areaof the block by a sensor communicating via a conventional Wi-Fi orEthernet network. MEMS devices integrated in the sensing components canperform energy harvesting in order to power the transmission of thesensor data over a network.

In embodiments, a system for data collection in an industrialenvironment having a self-sufficient data acquisition box for capturingand analyzing data in an industrial environment comprises a data circuitfor analyzing a plurality of sensor inputs, a network communicationinterface, a network control circuit for sending and receivinginformation related to the sensor inputs to an external system and adata filter circuit configured to dynamically adjust what portion of theinformation is sent based on instructions received over the networkcommunication interface. In embodiments, the data circuit is configuredto analyze data indicative of a fatigue or wear failure mode in a rollerbearing assembly such as rust, micropitting, macropitting, gear teethbreakage, fretting, case-core separation, plastic deformation, scuffing,polishing, adhesion, abrasion, subcase fatigue, erosion, corrosion,electric discharge, cavitation, cracking, scoring, profile pitting, andspalling.

In embodiments, the data circuit is configured to analyze dataindicative of a fatigue or wear failure mode in a gear box such asmicropitting, macropitting, gear tooth wear, tooth breakage, spalling,fretting, case-core separation, plastic deformation, scuffing,polishing, adhesion, abrasion, subcase fatigue, erosion, electricdischarge, cavitation, rust, corrosion, and cracking.

In embodiments, the data circuit is configured to analyze dataindicative of a fatigue or wear failure mode in a hydraulic pump such asfluid aeration, overheating, over-pressurization, lubricating film loss,depressurization, shaft failure, vacuum seal failure, large particlecontamination, small particle contamination, rust, corrosion,cavitation, shaft galling, seizure, bushing wear, channel seal loss, andimplosion.

In embodiments, the data circuit is configured to analyze dataindicative of a fatigue or wear failure mode in an engine such asimbalance, gasket failure, camshaft, spring breakage, valve breakage,valve scuffing, valve leakage, clutch slipping, gear interference, beltslipping, belt teeth breakage, belt breakage, gear tooth failure, oilseal failure, aftercooler, intercooler, or radiator failure, rodfailure, sensor failure, crankshaft failure, bearing seizure, overloadat low RPM, cranking, full stop, high RPM, overspeed, pistondisintegration, shock overload, torque overload, surface fatigue,critical speed failure, weld failure, and material failures includingmicropitting, macropitting, gear teeth breakage, fretting, case-coreseparation, plastic deformation, scuffing, polishing, adhesion,abrasion, subcase fatigue, rust, erosion, corrosion, electric discharge,cavitation, cracking, scoring, profile pitting and spalling.

In embodiments, the data circuit is configured to analyze dataindicative of a fatigue or wear failure mode in a vehicle chassis, bodyor frame such as imbalance, gasket failure, spring breakage, lubricantseal failure, sensor failure, bearing seizure, shock overload, surfacefatigue, weld failure, spring failure, strut failure, control armfailure, kingpin failure, tie-rod and end failure, pinion bearingfailure, pinion gear failure, and material failures includingmicropitting, macropitting, fretting, rust, erosion, corrosion, electricdischarge, cavitation, cracking, scoring, profile pitting and spalling.

In embodiments, the data circuit is configured to analyze dataindicative of a fatigue or wear failure mode in a powertrain, propellershaft, drive shaft, final drive, or wheel end, such as imbalance, gasketfailure, camshaft failure, gear box failure, spring breakage, valvebreakage, valve scuffing, belt teeth breakage, belt breakage, gear toothfailure, oil seal failure, rod failure, sensor failure, crankshaftfailure, bearing seizure, overload at low RPM, cranking, full stop, highRPM, overspeed, piston disintegration, shock overload, torque overload,surface fatigue, critical speed failure, yoke damage, weld failure,u-joint failure, CV joint failure, differential failure, axle shaftfailure, spring failure, strut failure, control arm failure, kingpinfailure, tie-rod & end failure, pinion bearing failure, ring gearfailure, pinion gear failure, spider gear failure, wheel bearingfailure, and material failures including micropitting, macropitting,gear teeth breakage, fretting, case-core separation, plasticdeformation, scuffing, polishing, adhesion, abrasion, subcase fatigue,rust, erosion, corrosion, electric discharge, cavitation, cracking,scoring, profile pitting and spalling.

In embodiments, the sensor input can be a roller-bearing sensor,deformation sensor, camera, ultraviolet sensor, infrared sensor, audiosensor, vibration sensor, viscosity sensor, chemical sensor, contaminantsensor, particulate sensor, weight sensor, rotation sensor, temperaturesensor, position sensor, ultrasonic sensor, solid chemical sensor, pHsensor, fluid chemical sensor, lubricant sensor, radiation sensor, x-rayradiograph, gamma-ray radiograph, scanning tunneling microscope,photon-tunneling microscope, scanning probe microscope, laserdisplacement meter, magnetic particle inspector, ultraviolet particledetector, load sensor, static load sensor, axial load sensor,accelerometer, speed sensor, rotational sensor, moisture, humidity,ammeter, voltmeter, flux meter, and electric field detector, gear boxsensor, gear wear sensor, “tooth decay” sensor, rotation sensors,transmission input sensor, transmission output sensor, manifold airflowsensor (determines engine load and thus affects gearbox), engine loadsensors, throttle position sensor, coolant temperature sensor, speedsensor, brake sensor, fluid temperature sensor, tool load sensor,bearing sensor, standstill counter, hydraulic pump sensor, oxygensensors, gas sensors, oil sensors, chemical analysis, pressure detector,vacuum detector, densitometer, torque sensor, engine sensor, exhaustsensors, exhaust gas sensor, crankshaft position sensor, camshaftposition sensor, capacitive pressure sensor, piezo-resistive sensor,wireless sensor, wireless pressure sensor, chemical sensors, oxygensensor, fuel sensor, gyro sensor, mechanical position sensors,accelerometer, mems sensors, digital sensors, mass air flow sensor,manifold absolute pressure sensor, throttle control sensor, injectorsensor, NOx sensor, variable valve timing sensor, tank pressure sensor,fuel level sensor, fuel flow sensor, fluid flow sensor, damper sensor,torque sensor, particulate sensor, air flow meter, air temperaturesensor, coolant temperature sensor, in-cylinder pressure sensor, enginespeed sensor, knock sensor, drive shaft sensor, angular sensor,transverse vibration sensor, torsional vibration sensor, critical speedvibration sensor, powertrain sensor, engine sensors: power sensor, oilpressure, oil temperature, oil viscosity, oil flow sensor, load sensor(structural analysis), vibration sensor, frequency sensor, audio sensor,transfer case sensor, differential sensor, tire pressure gauge, tiredamage gauge, tire vibration sensor, hub and rim integrity sensors, airleak sensors, fluid leak sensors, and lubricant leak sensors.

In embodiments, the sensor inputs additionally comprise microphones orvibration sensors configured to detect vibrational or audio-frequencyconditions in movable or rotational components, such as whirring,howling, growling, whining, rumbling, clunking, rattling, wheel hopping,and chattering.

In embodiments, the data circuit is configured to analyze dataindicative of a fatigue or wear failure mode in a production line gearbox, such as micropitting, macropitting, gear tooth wear, toothbreakage, spalling, fretting, case-core separation, plastic deformation,scuffing, polishing, adhesion, abrasion, subcase fatigue, erosion,electric discharge, cavitation, corrosion, and cracking.

In embodiments, the data circuit is configured to analyze dataindicative of a fatigue or wear failure mode in a production linevibrator such as moisture penetration, contamination, micropitting,macropitting, gear tooth wear, tooth breakage, spalling, fretting,case-core separation, plastic deformation, scuffing, polishing,adhesion, abrasion, subcase fatigue, rust, erosion, electric discharge,cavitation, corrosion, and cracking.

In embodiments, analyzing comprises detecting anomalies in the receiveddata. In embodiments, the data filter circuit executes stored proceduresto create digests of the information. In embodiments, the systemdiscards the data underlying the digests of the information after auser-configurable time period.

In embodiments, analyzing comprises determining what data to store,determining what data to transmit, determining what data to summarize,determining what data to discard, or determining the accuracy of thereceived data.

In embodiments, the system is configured to communicate with a pluralityof other similarly configured systems and store the information when theamount of storage used by the system exceeds a threshold.

In embodiments, the system is configured to execute the instructionsreceived via the network communication interface using a virtualmachine.

In embodiments, the system further comprises a digitally signed codeexecution environment to decrypt and run the instructions it receivesvia the network interface.

In embodiments, the system further comprises multiple distinctcryptographically protected memory segments.

In embodiments, the at least one of the memory segments is madeavailable for public interaction with the stored data via a publickey-private key management system.

In embodiments, the system further comprises a conditioning circuit forconverting signals to a form suitable for input to an analog-to-digitalconverter.

In embodiments, a system for data collection in an industrialenvironment having a self-sufficient data acquisition box for capturingand analyzing data in an industrial process, comprises a data circuitfor analyzing a plurality of sensor inputs, a network control circuitfor sending and receiving information related to the sensor inputs to anexternal system, and a storage device, where the data circuitcontinuously monitors sensor inputs and stores them in an embedded datacube and where the data acquisition box dynamically determines whatinformation to send based on statistical analysis of historical data.

In embodiments, the system further comprises a plurality of networkcommunication interfaces. In embodiments, the network control circuitbridges another similarly configured system from one network to anotherusing the plurality of network communication interfaces. In embodiments,the analyzing further comprises detecting anomalies in the information.In embodiments, the data circuit executes stored procedures to createdigests of the information. In embodiments, the data circuit suppliesdigest data to one client and non-digest data to another clientsimultaneously. In embodiments, the data circuit stores digests ofhistorical anomalies and discards at least a portion of the information.In embodiments, the data circuit provides client query access to theembedded data cube in real time. In embodiments, the data circuitsupports client requests in the form of a SQL query. In embodiments, thedata circuit supports client requests in the form of an OLAP query. Inembodiments, the system further comprises a conditioning circuit forconverting signals to a form suitable for input to an analog-to-digitalconverter.

In embodiments, a system for data collection in an industrialenvironment having a self-sufficient data acquisition box for capturingand analyzing data in an industrial process comprises a data circuit foranalyzing a plurality of sensor inputs, and a network control circuitfor sending and receiving information related to the sensor inputs to anexternal system, the system is configured to provide sensor data to aplurality of other similarly configured systems, and the systemdynamically reconfigures where it sends data and the and the quantity itsends based on the availability of the other similarly configuredsystems.

In embodiments, the system further comprises a plurality of networkcommunication interfaces. In embodiments, the network control circuitbridges another similarly configured system from one network to anotherusing the plurality of network communication interfaces. In embodiments,the dynamic reconfiguration is based on requests received over the oneor more network communication interfaces. In embodiments, the dynamicreconfiguration is based on requests made by a remote user. Inembodiments, the dynamic reconfiguration is based on an analysis of thetype of data acquired by the data acquisition box. In embodiments, thedynamic reconfiguration is based on an operating parameter of at leastone of the system and one of the similarly configured systems. Inembodiments, the network control circuit sends sensor data in packetsdesigned to be stored and forwarded by the other similarly configuredsystems. In embodiments, when a fault is detected in the system, thenetwork control circuit forwards a at least a portion of its storedinformation to another similarly configured system. In embodiments, thenetwork control circuit determines how to route information through anetwork of similarly configured systems connected, based on the sourceof the information request. In embodiments, the network control circuitdecides how to route data in a network of similarly configured systems,based on how frequently information is being requested. In embodiments,the network control circuit decides how to route data in a network ofsimilarly configured systems, based on how much data is being requestedover a given period. In embodiments, the network control circuitimplements a network of similarly configured systems using anintercommunication protocol such as multi-hop, mesh, serial, parallel,ring, real-time and hub-and-spoke. In embodiments, after a configurabletime period, the system stores only digests of the information anddiscards the underlying information. In embodiments, the system furthercomprises a conditioning circuit for converting signals to a formsuitable for input to an analog-to-digital converter.

In embodiments, a system for data collection in an industrialenvironment having a self-sufficient data acquisition box for capturingand analyzing data in an industrial process, comprises a data circuitfor analyzing a plurality of sensor inputs, a network control circuitfor sending and receiving information related to the sensor inputs to anexternal system, where the system provides sensor data to one or moresimilarly configured systems and where the data circuit dynamicallyreconfigures the route by which it sends data based on how many otherdevices are requesting the information.

In embodiments, the system further comprises a plurality of networkcommunication interfaces. In embodiments, the network control circuitbridges another similarly configured system from one network to anotherusing the plurality of network communication interfaces. In embodiments,the network control circuit implements a network of similarly configuredsystems using an intercommunication protocol such as multi-hop, mesh,serial, parallel, ring, real-time and hub-and-spoke. In embodiments, thesystem continuously provides a single copy of its information to anothersimilarly configured system and directs requesters of its information tothe another similarly configured system. In embodiments, the anothersimilarly configured system has different operational characteristicsthan the system. In embodiments, the different operationalcharacteristics can be power, storage, network connectivity, proximity,reliability, duty cycle. In embodiments, after a configurable timeperiod, the system stores only digests of the information and discardsthe underlying information.

In embodiments, a system for data collection in an industrialenvironment having a self-sufficient data acquisition box for capturingand analyzing data in an industrial process comprises a data circuit foranalyzing a plurality of sensor inputs, a network control circuit forsending and receiving information related to the sensor inputs to anexternal system, where the system provides sensor data to one or moresimilarly configured systems and where the data circuit dynamicallynominates a similarly configured system capable of providing sensor datato replace the system.

In embodiments, the nomination is triggered by the detection of a systemfailure mode. In embodiments, when the system is unable to supply arequested signal it nominates another similarly configured system tosupply similar but not identical information to a requestor. Inembodiments, the system indicates to the requestor that the new signalis different than the original. In embodiments, the network controlcircuit implements a network of similarly configured systems using anintercommunication protocol such as multi-hop, mesh, serial, parallel,ring, real-time and hub-and-spoke. In embodiments, after a configurabletime period, the system stores only digests of the information anddiscards the underlying information. In embodiments, the network controlcircuit self-arranges the system into a redundant storage network withone or more similarly configured systems. In embodiments, the networkcontrol circuit self-arranges the system into a fault-tolerant storagenetwork with one or more similarly configured systems. In embodiments,the network control circuit self-arranges the system into a hierarchicalstorage network with one or more similarly configured systems. Inembodiments, the network control circuit self-arranges the system into ahierarchical data transmission configuration in order to reduce upstreamtraffic. In embodiments, the network control circuit self-arranges thesystem into a matrixed network configuration with multiple redundantdata paths in order to increase reliability of information transmission.In embodiments, the network control circuit self-arranges the systeminto a matrixed network configuration with multiple redundant data pathsin order to increase reliability of information transmission. Inembodiments, the system accumulates data received from other similarlyconfigured systems while an upstream network connection is unavailable,and then sends all accumulated data once the upstream network connectionis restored. In embodiments, the accumulated data is committed to aremote database. In embodiments, the system rearranges its position in amesh network topology with other similarly configured systems in orderto minimize the amount of data it must relay from the other systems. Inembodiments, the system rearranges its position in a mesh networktopology with other similarly configured systems in order to minimizethe amount of data it must send through other the other systems.

In embodiments, a system for data collection in an industrialenvironment having a self-sufficient data acquisition box for capturingand analyzing data in an industrial process comprises a data circuit foranalyzing a plurality of sensor inputs, a network control circuit forsending and receiving information related to the sensor inputs to anexternal system, where the system provides sensor data to one or moresimilarly configured systems and where the system and the one or moresimilarly configured systems are arranged as a consolidated virtualinformation provider.

In embodiments, the system and each of the similarly configured systemsmultiplex their information. In embodiments, the system and each of thesimilarly configured systems provide a single unified information sourceto a requestor. In embodiments, the system and each of the similarlyconfigured systems further comprise an intelligent agent circuit thatcombines the data between systems. In embodiments, the system and eachof the similarly configured systems further comprise an intelligentagent circuit that chooses what data to collect or store based on amachine learning algorithm. In embodiments, the machine learningalgorithm further comprises a feedback function that takes as input whatdata is used by an external system. In embodiments, the machine learningalgorithm further comprises a control function that adjusts the degreeof precision, frequency of capture, or information stored based on ananalysis of requests for data over time. In embodiments, the machinelearning algorithm further comprises a feedback function that adjustswhat sensor data is captured based on an analysis of requests forinformation over time. In embodiments, the machine learning algorithmfurther comprises a feedback function that adjusts what sensor data iscaptured based on historical use of information. In embodiments, themachine learning algorithm further comprises a feedback function thatadjusts what sensor data is captured based on what information was mostindicative of a failure mode. In embodiments, the machine learningalgorithm further comprises a feedback function that adjusts what sensordata is captured based on detected combinations of informationcoincident with a failure mode. In embodiments, the network controlcircuit implements a network of similarly configured systems using anintercommunication protocol such as multi-hop, mesh, serial, parallel,ring, real-time and hub-and-spoke. In embodiments, the network controlcircuit self-arranges the system into network communication withsimilarly configured systems using an intercommunication protocol suchas multi-hop, mesh, serial, parallel, ring, real-time and hub-and-spoke.In embodiments, after a configurable time period, the system stores onlydigests of the information and discards the underlying information.

A system for data collection in an industrial environment having aself-sufficient data acquisition box for capturing and analyzing data inan industrial environment, the system comprising: a data circuit foranalyzing a plurality of sensor inputs; a network communicationinterface; a network control circuit for sending and receivinginformation related to the sensor inputs to an external system; and adata filter circuit configured to dynamically adjust what portion of theinformation is sent based on instructions received over the networkcommunication interface.

Wherein the data circuit is configured to analyze data indicative of afatigue or wear failure mode in a roller bearing assembly selected fromthe group consisting of rust, micropitting, macropitting, gear teethbreakage, fretting, case-core separation, plastic deformation, scuffing,polishing, adhesion, abrasion, subcase fatigue, erosion, corrosion,electric discharge, cavitation, cracking, scoring, profile pitting, andspalling.

Wherein the data circuit is configured to analyze data indicative of afatigue or wear failure mode in a gear box selected from the groupconsisting of micropitting, macropitting, gear tooth wear, toothbreakage, spalling, fretting, case-core separation, plastic deformation,scuffing, polishing, adhesion, abrasion, subcase fatigue, erosion,electric discharge, cavitation, rust, corrosion, and cracking.

Wherein the data circuit is configured to analyze data indicative of afatigue or wear failure mode in a hydraulic pump selected from the groupconsisting of fluid aeration, overheating, over-pressurization,lubricating film loss, depressurization, shaft failure, vacuum sealfailure, large particle contamination, small particle contamination,rust, corrosion, cavitation, shaft galling, seizure, bushing wear,channel seal loss, and implosion.

Wherein the data circuit is configured to analyze data indicative of afatigue or wear failure mode in an engine selected from the groupconsisting of imbalance, gasket failure, camshaft, spring breakage,valve breakage, valve scuffing, valve leakage, clutch slipping, gearinterference, belt slipping, belt teeth breakage, belt breakage, geartooth failure, oil seal failure, aftercooler, intercooler, or radiatorfailure, rod failure, sensor failure, crankshaft failure, bearingseizure, overload at low RPM, cranking, full stop, high RPM, overspeed,piston disintegration, shock overload, torque overload, surface fatigue,critical speed failure, weld failure, and material failures includingmicropitting, macropitting, gear teeth breakage, fretting, case-coreseparation, plastic deformation, scuffing, polishing, adhesion,abrasion, subcase fatigue, rust, erosion, corrosion, electric discharge,cavitation, cracking, scoring, profile pitting, spalling.

Wherein the data circuit is configured to analyze data indicative of afatigue or wear failure mode in a vehicle chassis, body or frameselected from the group consisting of imbalance, gasket failure, springbreakage, lubricant seal failure, sensor failure, bearing seizure, shockoverload, surface fatigue, weld failure, spring failure, strut failure,control arm failure, kingpin failure, tie-rod & end failure, pinionbearing failure, pinion gear failure, and material failures includingmicropitting, macropitting, fretting, rust, erosion, corrosion, electricdischarge, cavitation, cracking, scoring, profile pitting, spalling.

Wherein the data circuit is configured to analyze data indicative of afatigue or wear failure mode in a powertrain, propeller shaft, driveshaft, final drive, or wheel end, selected from the group consisting ofimbalance, gasket failure, camshaft failure, gear box failure, springbreakage, valve breakage, valve scuffing, belt teeth breakage, beltbreakage, gear tooth failure, oil seal failure, rod failure, sensorfailure, crankshaft failure, bearing seizure, overload at low RPM,cranking, full stop, high RPM, overspeed, piston disintegration, shockoverload, torque overload, surface fatigue, critical speed failure, yokedamage, weld failure, u-joint failure, CV joint failure, differentialfailure, axle shaft failure, spring failure, strut failure, control armfailure, kingpin failure, tie-rod & end failure, pinion bearing failure,ring gear failure, pinion gear failure, spider gear failure, wheelbearing failure, and material failures including micropitting,macropitting, gear teeth breakage, fretting, case-core separation,plastic deformation, scuffing, polishing, adhesion, abrasion, subcasefatigue, rust, erosion, corrosion, electric discharge, cavitation,cracking, scoring, profile pitting, and spalling.

Wherein the sensor inputs are selected from the group consisting ofroller bearing sensor, deformation sensor, camera, ultraviolet sensor,infrared sensor, audio sensor, vibration sensor, viscosity sensor,chemical sensor, contaminant sensor, particulate sensor, weight sensor,rotation sensor, temperature sensor, position sensor, ultrasonic sensor,solid chemical sensor, pH sensor, fluid chemical sensor, lubricantsensor, radiation sensor, x-ray radiograph, gamma-ray radiograph,scanning tunneling microscope, photon tunneling microscope, scanningprobe microscope, laser displacement meter, magnetic particle inspector,ultraviolet particle detector, load sensor, static load sensor, axialload sensor, accelerometer, speed sensor, rotational sensor, moisture,humidity, ammeter, voltmeter, flux meter, and electric field detector,gear box sensor, gear wear sensor, “tooth decay” sensor, rotationsensors, transmission input sensor, transmission output sensor, manifoldairflow sensor (determines engine load and thus affects gearbox), engineload sensors, throttle position sensor, coolant temperature sensor,speed sensor, brake sensor, fluid temperature sensor, tool load sensor,bearing sensor, standstill counter, hydraulic pump sensor, oxygensensors, gas sensors, oil sensors, chemical analysis, pressure detector,vacuum detector, densitometer, torque sensor, engine sensor, exhaustsensors, exhaust gas sensor, crankshaft position sensor, camshaftposition sensor, capacitive pressure sensor, piezo-resistive sensor,wireless sensor, wireless pressure sensor, chemical sensors, oxygensensor, fuel sensor, gyro sensor, mechanical position sensors,accelerometer, mems sensors, digital sensors, mass air flow sensor,manifold absolute pressure sensor, throttle control sensor, injectorsensor, NOx sensor, variable valve timing sensor, tank pressure sensor,fuel level sensor, fuel flow sensor, fluid flow sensor, damper sensor,torque sensor, particulate sensor, air flow meter, air temperaturesensor, coolant temperature sensor, in-cylinder pressure sensor, enginespeed sensor, knock sensor, drive shaft sensor, angular sensor,transverse vibration sensor, torsional vibration sensor, critical speedvibration sensor, powertrain sensor, engine sensors: power sensor, oilpressure, oil temperature, oil viscosity, oil flow sensor, load sensor(structural analysis), vibration sensor, frequency sensor, audio sensor,transfer case sensor, differential sensor, tire pressure gauge, tiredamage gauge, tire vibration sensor, hub and rim integrity sensors, airleak sensors, fluid leak sensors, and lubricant leak sensors.

Wherein the sensor inputs additionally comprise microphones or vibrationsensors configured to detect vibrational or audio-frequency conditionsin movable or rotational components selected from the list consisting ofwhirring, howling, growling, whining, rumbling, clunking, rattling,wheel hopping, chattering.

Wherein the data circuit is configured to analyze data indicative of afatigue or wear failure mode in a production line gear box selected fromthe group consisting of micropitting, macropitting, gear tooth wear,tooth breakage, spalling, fretting, case-core separation, plasticdeformation, scuffing, polishing, adhesion, abrasion, subcase fatigue,erosion, electric discharge, cavitation, corrosion, and cracking.

Wherein the data circuit is configured to analyze data indicative of afatigue or wear failure mode in a production line vibrator selected fromthe group consisting of moisture penetration, contamination,micropitting, macropitting, gear tooth wear, tooth breakage, spalling,fretting, case-core separation, plastic deformation, scuffing,polishing, adhesion, abrasion, subcase fatigue, rust, erosion, electricdischarge, cavitation, corrosion, and cracking.

Wherein the analyzing further comprises detecting anomalies in thereceived data.

Wherein the data filter circuit executes stored procedures to createdigests of the information.

Wherein the system discards the data underlying the digests of theinformation after a user-configurable time period.

Wherein the analyzing further comprises determining what data to store,determining what data to transmit, determining what data to summarize,determining what data to discard, or determining the accuracy of thereceived data.

Wherein the system is configured to communicate with a plurality ofother similarly configured systems and store the information when theamount of storage used by the system exceeds a threshold.

Wherein the system is configured to execute the instructions receivedvia the network communication interface using a virtual machine.

Wherein the system further comprises a digitally signed code executionenvironment to decrypt and run the instructions it receives via thenetwork interface.

Wherein the system further comprises multiple distinct cryptographicallyprotected memory segments.

Wherein the at least one of the memory segments is made available forpublic interaction with the stored data via a public key-private keymanagement system.

Wherein the system further comprises a conditioning circuit forconverting signals to a form suitable for input to an analog-to-digitalconverter.

A system for data collection in an industrial environment having aself-sufficient data acquisition box for capturing and analyzing data inan industrial process, the system comprising:

a data circuit for analyzing a plurality of sensor inputs;a network control circuit for sending and receiving information relatedto the sensor inputs to an external system;a storage device;where the data circuit continuously monitors sensor inputs and storesthem in an embedded data cube; andwhere the data acquisition box dynamically determines what informationto send based on statistical analysis of historical data.

Wherein the system further comprises a plurality of networkcommunication interfaces.

Wherein the network control circuit bridges another similarly configuredsystem from one network to another using the plurality of networkcommunication interfaces.

Wherein the analyzing further comprises detecting anomalies in theinformation.

Wherein the data circuit executes stored procedures to create digests ofthe information.

Wherein the data circuit supplies digest data to one client andnon-digest data to another client simultaneously.

Wherein the data circuit stores digests of historical anomalies anddiscards at least a portion of the information.

Wherein the data circuit provides client query access to the embeddeddata cube in real time.

Wherein the data circuit supports client requests in the form of a SQLquery.

Wherein the data circuit supports client requests in the form of an OLAPquery.

Wherein the system further comprises a conditioning circuit forconverting signals to a form suitable for input to an analog-to-digitalconverter.

A system for data collection in an industrial environment having aself-sufficient data acquisition box for capturing and analyzing data inan industrial process, the system comprising:

a data circuit for analyzing a plurality of sensor inputs;a network control circuit for sending and receiving information relatedto the sensor inputs to an external system;wherein the system is configured to provide sensor data to a pluralityof other similarly configured systems; andwherein the system dynamically reconfigures where it sends data and thequantity it sends based on the availability of the other similarlyconfigured systems.

Wherein the system further comprises a plurality of networkcommunication interfaces.

Wherein the network control circuit bridges another similarly configuredsystem from one network to another using the plurality of networkcommunication interfaces.

Wherein the dynamic reconfiguration is based on requests received overthe one or more network communication interfaces.

Wherein the dynamic reconfiguration is based on requests made by aremote user.

Wherein the dynamic reconfiguration is based on an analysis of the typeof data acquired by the data acquisition box.

Wherein the dynamic reconfiguration is based on an operating parameterof at least one of the system and one of the similarly configuredsystems.

Wherein the network control circuit sends sensor data in packetsdesigned to be stored and forwarded by the other similarly configuredsystems.

Wherein, when a fault is detected in the system, the network controlcircuit forwards at least a portion of its stored information for toanother similarly configured system.

Wherein the network control circuit determines how to route informationthrough a network of similarly configured systems connected, based onthe source of the information request.

Wherein the network control circuit decides how to route data in anetwork of similarly configured systems, based on how frequentlyinformation is being requested.

Wherein the decides how to route data in a network of similarlyconfigured systems, based how much data is being requested over a givenperiod.

Wherein the network control circuit implements a network of similarlyconfigured systems using an intercommunication protocol selected fromthe list consisting of multi-hop, mesh, serial, parallel, ring,real-time and hub-and-spoke.

Wherein, after a configurable time period, the system stores onlydigests of the information and discards the underlying information.

Wherein the system further comprises a conditioning circuit forconverting signals to a form suitable for input to an analog-to-digitalconverter.

A system for data collection in an industrial environment having aself-sufficient data acquisition box for capturing and analyzing data inan industrial process, the system comprising:

a data circuit for analyzing a plurality of sensor inputs;a network control circuit for sending and receiving information relatedto the sensor inputs to an external system;wherein the system provides sensor data to one or more similarlyconfigured systems;wherein the data circuit dynamically reconfigures the route by which itsends data based on how many other devices are requesting theinformation.

Wherein the system further comprises a plurality of networkcommunication interfaces.

Wherein the network control circuit bridges another similarly configuredsystem from one network to another using the plurality of networkcommunication interfaces.

Where the network control circuit implements a network of similarlyconfigured systems using an intercommunication protocol selected fromthe list consisting of multi-hop, mesh, serial, parallel, ring,real-time and hub-and-spoke.

Wherein the system continuously provides a single copy of itsinformation to another similarly configured system and directsrequesters of its information to the another similarly configuredsystem.

Wherein the another similarly configured system has differentoperational characteristics than the system.

Wherein different operational characteristics are selected from the listconsisting of power, storage, network connectivity, proximity,reliability, and duty cycle.

Wherein, after a configurable time period, the system stores onlydigests of the information and discards the underlying information.

A system for data collection in an industrial environment having aself-sufficient data acquisition box for capturing and analyzing data inan industrial process, the system comprising:

a data circuit for analyzing a plurality of sensor inputs;a network control circuit for sending and receiving information relatedto the sensor inputs to an external system;wherein the system provides sensor data to one or more similarlyconfigured systems; andwherein the data circuit dynamically nominates a similarly configuredsystem capable of providing sensor data to replace the system.

Wherein the nomination is triggered by the detection of a system failuremode.

Wherein, when the system is unable to supply a requested signal itnominates another similarly configured system to supply similar but notidentical information to a requestor.

Wherein the system indicates to the requestor that the new signal isdifferent than the original.

Where the network control circuit implements a network of similarlyconfigured systems using an intercommunication protocol selected fromthe list consisting of multi-hop, mesh, serial, parallel, ring,real-time and hub-and-spoke.

Wherein, after a configurable time period, the system stores onlydigests of the information and discards the underlying information.

Wherein the network control circuit self-arranges the system into aredundant storage network with one or more similarly configured systems.

Wherein the network control circuit self-arranges the system into afault-tolerant storage network with one or more similarly configuredsystems.

Wherein the network control circuit self-arranges the system into ahierarchical storage network with one or more similarly configuredsystems.

Wherein the network control circuit self-arranges the system into ahierarchical data transmission configuration in order to reduce upstreamtraffic.

Wherein the network control circuit self-arranges the system into amatrixed network configuration with multiple redundant data paths inorder to increase reliability of information transmission.

Wherein the network control circuit self-arranges the system into amatrixed network configuration with multiple redundant data paths inorder to increase reliability of information transmission.

Wherein the system accumulates data received from other similarlyconfigured systems while an upstream network connection is unavailable,and then sends all accumulated data once the upstream network connectionis restored.

Wherein the accumulated data is committed to a remote database.

Wherein the system rearranges its position in a mesh network topologywith other similarly configured systems in order to minimize the amountof data it must relay from the other systems.

Wherein the system rearranges its position in a mesh network topologywith other similarly configured systems in order to minimize the amountof data it must send through the other systems.

A system for data collection in an industrial environment having aself-sufficient data acquisition box for capturing and analyzing data inan industrial process, the system comprising:

a data circuit for analyzing a plurality of sensor inputs;a network control circuit for sending and receiving information relatedto the sensor inputs to an external system;wherein the system provides sensor data to one or more similarlyconfigured systems; andwherein the system and the one or more similarly configured systems arearranged as a consolidated virtual information provider.

Wherein the system and each of the similarly configured systemsmultiplex their information.

Wherein the system and each of the similarly configured systems providea single unified information source to a requestor.

Wherein the system and each of the similarly configured systems furthercomprise an intelligent agent circuit that combines the data betweensystems.

Wherein the system and each of the similarly configured systems furthercomprise an intelligent agent circuit that chooses what data to collector store based on a machine learning algorithm.

Wherein the machine learning algorithm further comprises a feedbackfunction that takes as input what data is used by an external system.

Wherein the machine learning algorithm further comprises a controlfunction that adjusts the degree of precision, frequency of capture, orinformation stored based on an analysis of requests for data over time.

Wherein the machine learning algorithm further comprises a feedbackfunction that adjusts what sensor data is captured based on an analysisof requests for information over time.

Wherein the machine learning algorithm further comprises a feedbackfunction that adjusts what sensor data is captured based on historicaluse of information.

Wherein the machine learning algorithm further comprises a feedbackfunction that adjusts what sensor data is captured based on whatinformation was most indicative of a failure mode.

Wherein the machine learning algorithm further comprises a feedbackfunction that adjusts what sensor data is captured based on detectedcombinations of information coincident with a failure mode.

Wherein the network control circuit implements a network of similarlyconfigured systems using an intercommunication protocol selected fromthe list consisting of multi-hop, mesh, serial, parallel, ring,real-time and hub-and-spoke.

Wherein the network control circuit self-arranges the system intonetwork communication with similarly configured systems using anintercommunication protocol selected from the list consisting ofmulti-hop, mesh, serial, parallel, ring, real-time and hub-and-spoke.

Wherein, after a configurable time period, the system stores onlydigests of the information and discards the underlying information.

Disclosed herein are methods and systems for data collection in anindustrial environment featuring self-organization functionality. Suchdata collection systems and methods may facilitate intelligent,situational, context-aware collection, summarization, storage,processing, transmitting, and/or organization of data, such as by one ormore data collectors (such as any of the wide range of data collectorembodiments described throughout this disclosure), a centralheadquarters or computing system, and the like. The describedself-organization functionality of data collection in an industrialenvironment may improve various parameters of such data collection, aswell as parameters of the processes, applications, and products thatdepend on data collection, such as data quality parameters, consistencyparameters, efficiency parameters, comprehensiveness parameters,reliability parameters, effectiveness parameters, storage utilizationparameters, yield parameters (including financial yield, output yield,and reduction of adverse events), energy consumption parameters,bandwidth utilization parameters, input/output speed parameters,redundancy parameters, security parameters, safety parameters,interference parameters, signal-to-noise parameters, statisticalrelevancy parameters, and others. The self-organization functionalitymay optimize across one or more such parameters, such as based on aweighting of the value of the parameters; for example, a swarm of datacollectors may be managed (or manage itself) to provide a given level ofredundancy for critical data, while not exceeding a specified level ofenergy usage, e.g., per data collector or a group of data collectors orthe entire swarm of data collectors. This may include using a variety ofoptimization techniques described throughout this disclosure and thedocuments incorporated herein by reference.

In embodiments, such methods and systems for data collection in anindustrial environment can include one or more data collectors, e.g.,arranged in a cooperative group or “swarm” of data collectors, thatcollect and organize data in conjunction with a data pool incommunication with a computing system, as well as supporting technologycomponents, services, processes, modules, applications and interfaces,for managing the data collection (collectively referred to in some casesas a data collection system 12004). Examples of such components include,but are not limited to, a model-based expert system, a rule-based expertsystem, an expert system using artificial intelligence (such as amachine learning system, which may include a neural net expert system, aself-organizing map system, a human-supervised machine learning system,a state determination system, a classification system, or otherartificial intelligence system), or various hybrids or combinations ofany of the above. References to a self-organizing method or systemshould be understood to encompass utilization of any one of theforegoing or suitable combinations, except where context indicatesotherwise.

The data collection systems and methods of the present disclosure can beutilized with various types of data, including but not limited tovibration data, noise data and other sensor data of the types describedthroughout this disclosure. Such data collection can be utilized forevent detection, state detection, and the like, and such eventdetection, state detection, and the like can be utilized toself-organize the data collection systems and methods, as furtherdiscussed herein. The self-organization functionality may includemanaging data collector(s), both individually or in groups, where suchfunctionality is directed at supporting an identified application,process, or workflow, such as confirming progress toward or/alignmentwith one or more objectives, goals, rules, policies, or guidelines. Theself-organization functionality may also involve managing a differentgoal/guideline, or directing data collectors targeted to determining anunknown variable based on collection of other data (such as based on amodel of the behavior of a system that involves the variable), selectingpreferred sensor inputs among available inputs (including specifyingcombinations, fusions, or multiplexing of inputs), and/or specifying aspecific data collector among available data collectors.

A data collector may include any number of items, such as sensors, inputchannels, data locations, data streams, data protocols, data extractiontechniques, data transformation techniques, data loading techniques,data types, frequency of sampling, placement of sensors, static datapoints, metadata, fusion of data, multiplexing of data, self-organizingtechniques, and the like as described herein. Data collector settingsmay describe the configuration and makeup of the data collector, such asby specifying the parameters that define the data collector. Forexample, data collector settings may include one or more frequencies tomeasure. Frequency data may further include at least one of a group ofspectral peaks, a true-peak level, a crest factor derived from a timewaveform, and an overall waveform derived from a vibration envelope, aswell as other signal characteristics described throughout thisdisclosure. Data collectors may include sensors measuring or dataregarding one or more wavelengths, one or more spectra, and/or one ormore types of data from various sensors and metadata. Data collectorsmay include one or more sensors or types of sensors of a wide range oftypes, such as described throughout this disclosure and the documentsincorporated by reference herein. Indeed, the sensors described hereinmay be used in any of the methods or systems described throughout thisdisclosure. For example, one sensor may be an accelerometer, such as onethat measures voltage per G of acceleration (e.g., 100 mV/G, 500 mV/G, 1V/G, 5 V/G, 10 V/G). In embodiments, a data collector may alter themakeup of the subset of the plurality of sensors used in a datacollector based on optimizing the responsiveness of the sensor, such asfor example choosing an accelerometer better suited for measuringacceleration of a lower speed gear system or drill/boring device versusone better suited for measuring acceleration of a higher speed turbinein a power generation environment. Choosing may be done intelligently,such as for example with a proximity probe and multiple accelerometersdisposed on a specific target (e.g., a gear system, drill, or turbine)where while at low speed one accelerometer is used for measuring in thedata collector and another is used at high speeds. Accelerometers comein various types, such as piezo-electric crystal, low frequency (e.g.,10 V/G), high speed compressors (10 MV/G), MEMS, and the like. Inanother example, one sensor may be a proximity probe which can be usedfor sleeve or tilt-pad bearings (e.g., oil bath), or a velocity probe.In yet another example, one sensor may be a solid state relay (SSR) thatis structured to automatically interface with another routed datacollector (such as a mobile or portable data collector) to obtain ordeliver data. In another example, a data collector may be routed toalter the makeup of the plurality of available sensors, such as bybringing an appropriate accelerometer to a point of sensing, such as onor near a component of a machine. In still another example, one sensormay be a triax probe (e.g., a 100 MV/G triax probe), that in embodimentsis used for portable data collection. In some embodiments, of a triaxprobe, a vertical element on one axis of the probe may have a highfrequency response while the ones mounted horizontally may influencelimit the frequency response of the whole triax. In another example, onesensor may be a temperature sensor and may include a probe with atemperature sensor built inside, such as to obtain a bearingtemperature. In still additional examples, sensors may be ultrasonic,microphone, touch, capacitive, vibration, acoustic, pressure, straingauges, thermographic (e.g., camera), imaging (e.g., camera, laser, IR,structured light), a field detector, an EMF meter to measure an ACelectromagnetic field, a gaussmeter, a motion detector, a chemicaldetector, a gas detector, a CBRNE detector, a vibration transducer, amagnetometer, positional, location-based, a velocity sensor, adisplacement sensor, a tachometer, a flow sensor, a level sensor, aproximity sensor, a pH sensor, a hygrometer/moisture sensor, adensitometric sensor, an anemometer, a viscometer, or any analogindustrial sensor and/or digital industrial sensor. In a furtherexample, sensors may be directed at detecting or measuring ambientnoise, such as a sound sensor or microphone, an ultrasound sensor, anacoustic wave sensor, and an optical vibration sensor (e.g., using acamera to see oscillations that produce noise). In still anotherexample, one sensor may be a motion detector.

Data collectors may be of or may be configured to encompass one or morefrequencies, wavelengths or spectra for particular sensors, forparticular groups of sensors, or for combined signals from multiplesensors (such as involving multiplexing or sensor fusion). Datacollectors may be of or may be configured to encompass one or moresensors or sensor data (including groups of sensors and combinedsignals) from one or more pieces of equipment/components, areas of aninstallation, disparate but interconnected areas of an installation(e.g., a machine assembly line and a boiler room used to power theline), or locations (e.g., a building in one geographic location and abuilding in a separate, different geographic location). Data collectorsettings, configurations, instructions, or specifications (collectivelyreferred to herein using any one of those terms) may include where toplace a sensor, how frequently to sample a data point or points, thegranularity at which a sample is taken (e.g., a number of samplingpoints per fraction of a second), which sensor of a set of redundantsensors to sample, an average sampling protocol for redundant sensors,and any other aspect that would affect data acquisition.

Within the data collection system 12004, the self-organizationfunctionality can be implemented by a neural net, a model-based system,a rule-based system, a machine learning system, and/or a hybrid of anyof those systems. Further, the self-organizing functionality may beperformed in whole or in part by individual data collectors, acollection or group of data collectors, a network-based computingsystem, a local computing system comprising one or more computingdevices, a remote computing system comprising one or more computingdevices, and a combination of one or more of these components. Theself-organization functionality may be optimized for a particular goalor outcome, such as predicting and managing performance, health, orother characteristics of a piece of equipment, a component, or a systemof equipment or components. Based on continuous or periodic analysis ofsensor data, as patterns/trends are identified, or outliers appear, or agroup of sensor readings begin to change, etc., the self-organizationfunctionality may modify the collection of data intelligently, asdescribed herein. This may occur by triggering a rule that reflects amodel or understanding of system behavior (e.g., recognizing a shift inoperating mode that calls for different sensors as velocity of a shaftincreases) or it may occur under control of a neural net (either incombination with a rule-based approach or on its own), where inputs areprovided such that the neural net over time learns to select appropriatecollection modes based on feedback as to successful outcomes (e.g.,successful classification of the state of a system, successfulprediction, successful operation relative to a metric). In manyexamples, when an assembly line is reconfigured for a new product or anew assembly line is installed in a manufacturing facility, data fromthe current data collector(s) may not accurately predict the state ormetric of operation of the system, thus, the self-organizationfunctionality may begin to iterate to determine if a new data collector,type of sensed data, format of sensed data, etc. is better at predictinga state or metric. Based on offset system data, such as from a libraryor other data structure, certain sensors, frequency bands or other datacollectors may be used in the system initially and data may be collectedto assess performance. As the self-organization functionality iterates,other sensors/frequency bands may be accessed to determine theirrelative weight in identifying performance metrics. Over time, a newfrequency band may be identified (or a new collection of sensors, a newset of configurations for sensors, or the like) as a better or moresuitable gauge of performance in the system and the self-organizationfunctionality may modify its data collector(s) based on this iteration.In many examples, perhaps an older boring tool in an energy extractionenvironment dampens one or more vibration frequencies while a differentfrequency is of higher amplitude and present during optimal performancethan what was seen in the present system. In this example, theself-organization functionality may alter the data collectors from whatwas originally proposed, e.g., by the data collection system, to capturethe higher amplitude frequency that is present in the current system.

The self-organization functionality, in embodiments involving a neuralnet or other machine learning system, may be seeded and may iterate,e.g., based on feedback and operation parameters, such as describedherein. Certain feedback may include utilization measures, efficiencymeasures (e.g., power or energy utilization, use of storage, use ofbandwidth, use of input/output use of perishable materials, use of fuel,and/or financial efficiency, financial such as reduction of costs),measures of success in prediction or anticipation of states (e.g.,avoidance and mitigation of faults), productivity measures (e.g.,workflow), yield measures, and profit measures. Certain parameters mayinclude storage parameters (e.g., data storage, fuel storage, storage ofinventory), network parameters (e.g., network bandwidth, input/outputspeeds, network utilization, network cost, network speed, networkavailability), transmission parameters (e.g., quality of transmission ofdata, speed of transmission of data, error rates in transmission, costof transmission), security parameters (e.g., number and/or type ofexposure events, vulnerability to attack, data loss, data breach, accessparameters), location and positioning parameters (e.g., location of datacollectors, location of workers, location of machines and equipment,location of inventory units, location of parts and materials, locationof network access points, location of ingress and egress points,location of landing positions, location of sensor sets, location ofnetwork infrastructure, location of power sources), input selectionparameters, data combination parameters (e.g., for multiplexing,extraction, transformation, loading), power parameters (e.g., ofindividual data collectors, groups of data collectors, or allpotentially available data collectors), states (e.g., operational modes,availability states, environmental states, fault modes, health states,maintenance modes, anticipated states), events, and equipmentspecifications. With respect to states, operating modes may include,mobility modes (direction, speed, acceleration, and the like), type ofmobility modes (e.g., rolling, flying, sliding, levitation, hovering,floating), performance modes (e.g., gears, rotational speeds, heatlevels, assembly line speeds, voltage levels, frequency levels), outputmodes, fuel conversion modes, resource consumption modes, and financialperformance modes (e.g., yield, profitability). Availability states mayrefer to anticipating conditions that could cause machines to go offlineor require backup. Environmental states may refer to ambienttemperature, ambient humidity/moisture, ambient pressure, ambientwind/fluid flow, presence of pollution or contaminants, presence ofinterfering elements (e.g., electrical noise, vibration), poweravailability, and power quality, among other parameters. Anticipatedstates may include achieving or not achieving a desired goal, such as aspecified/threshold output production rate, a specified/thresholdgeneration rate, an operational efficiency/failure rate, a financialefficiency/profit goal, a power efficiency/resource utilization, anavoidance of a fault condition (e.g., overheating, slow performance,excessive speed, excessive motion, excessive vibration/oscillation,excessive acceleration, expansion/contraction, electrical failure,running out of stored power/fuel, overpressure, excessive radiation/meltdown, fire, freezing, failure of fluid flow (e.g., stuck valves, frozenfluids), mechanical failures (e.g., broken component, worn component,faulty coupling, misalignment, asymmetries/deflection, damaged component(e.g., deflection, strain, stress, cracking), imbalances, collisions,jammed elements, and lost or slipping chain or belt), avoidance of adangerous condition or catastrophic failure, and availability (onlinestatus)).

The self-organization functionality may comprise or be seeded with amodel that predicts an outcome or state given a set of data, which maycomprise inputs from sensors, such as via a data collector, as well asother data, such as from system components, from external systems andfrom external data sources. For example, the model may be an operatingmodel for an industrial environment, machine, or workflow. In anotherexample, the model may be for anticipating states, for predicting fault,for optimizing maintenance, for optimizing data transport (such as foroptimizing network coding, network-condition-sensitive routing), foroptimizing data marketplaces, and the like.

The self-organization functionality may result in any number ofdownstream actions based on analysis of data from the data collector(s).In an embodiment, the self-organization functionality may determine thatthe system should either keep or modify operational parameters,equipment or a weighting of a neural net model given a desired goal,such as a specified/threshold output production rate,specified/threshold generation rate, an operational efficiency/failurerate, a financial efficiency/profit goal, a power efficiency/resourceutilization, an avoidance of a fault condition, an avoidance of adangerous condition or catastrophic failure, and the like. Inembodiments, the adjustments may be based on determining context of anindustrial system, such as understanding a type of equipment, itspurpose, its typical operating modes, the functional specifications forthe equipment, the relationship of the equipment to other features ofthe environment (including any other systems that provide input to ortake input from the equipment), the presence and role of operators(including humans and automated control systems), and ambient orenvironmental conditions. For example, in order to achieve a profit goalin a distribution environment (e.g., a power distribution environment),a generator or system of generators may need to operate at a certainefficiency level. The self-organization functionality may be seeded witha model for operation of the system of generators in a manner thatresults in a specified profit goal, such as indicating an on/off statefor individual generator(s) in the power generation system based on thetime of day, current market sale price for the fuel consumed by thegenerators, current demand or anticipated future demand, and the like.As it acquires data and iterates, the model predicts whether the profitgoal will be achieved given the current data, and determine whether thedata or type of data being collected is appropriate, sufficient, etc.for the model. Based on the results of the iteration, a recommendationmay be made (or a control instruction may be automatically provided) togather different/additional data, organize the data differently, directdifferent data collectors to collect new data, etc. and/or to operate asubset of the generators at a higher output (but less efficient) rate,power on additional generators, maintain a current operational state, orthe like. Further, as the system iterates, one or more additionalsensors may be sampled in the model to determine if their addition tothe self-organization functionality would improve predicting a state orotherwise assisting with the goals of the data collection efforts.

In embodiments, a system for data collection in an industrialenvironment may include a plurality of input sensors, such as any ofthose described herein, communicatively coupled to a data collectorhaving one or more processors. The data collection system may include aplurality of individual data collectors structured to operate togetherto determine at least one subset of the plurality of sensors from whichto process output data. The data collection system may also include amachine learning circuit structured to receive output data from the atleast one subset of the plurality of sensors and learn received outputdata patterns indicative of a state. In some embodiments, the datacollection system may alter the at least one subset of the plurality ofsensors, or an aspect thereof, based on one or more of the learnedreceived output data patterns and the state. In certain embodiments, themachine learning circuit is seeded with a model that enables it to learndata patterns. The model may be a physical model, an operational model,a system model, and the like. In other embodiments, the machine learningcircuit is structured for deep learning. In embodiments, input data isfed to the circuit with no or minimal seeding and the machine learningdata analysis circuit learns based on output feedback. For example, ametal tooling system in a manufacturing environment may operate tomanufacture parts using machine tools such as lathes, milling machines,grinding machines, boring tools, and the like. Such machines may operateat various speeds and output rates, which may affect the longevity,efficiency, accuracy, etc. of the machine. The data collector mayacquire various parameters to evaluate the environment of the machinetools, e.g., speed of operation, heat generation, vibration, andconformity with a part specification. The system can utilize suchparameters and iterate towards a prediction of state, output rate, etc.based on such feedback. Further, the system may self-organize such thatthe data collector(s) collect additional/different data from which suchpredictions may be made.

There may be a balance of multiple goals/guidelines in theself-organization functionality of data collection system. For example,a repair and maintenance organization (RMO) may have operatingparameters designed for maintenance of a machine in a manufacturingfacility, while the owner of the facility may have particular operatingparameters for the machine that are designed for meeting a productiongoal. These goals, in this example relating to a maintenance goal or aproduction output, may be tracked by different data collectors orsensors. For example, maintenance of a machine may be tracked by sensorsincluding a temperature sensor, a vibration transducer, and a straingauge while the production goal of a machine may be tracked by sensorsincluding a speed sensor and a power consumption meter. The datacollection system may (optionally using a neural net, machine learningsystem, deep learning system, or the like, which may occur undersupervision by one or more supervisors (human or automated))intelligently manage data collectors aligned with different goals andassign weights, parameter modifications, or recommendations based on afactor, such as a bias towards one goal or a compromise to allow betteralignment with all goals being tracked, for example. Compromises amongthe goals delivered to the data collection system may be based on one ormore hierarchies or rules relating to the authority, role, criticality,or the like of the applicable goals. In embodiments, compromises amonggoals may be optimized using machine learning, such as a neural net,deep learning system, or other artificial intelligence system asdescribed throughout this disclosure. For example, in a power plantwhere a turbine is operating, the data collection system may managemultiple data collectors, such as one directed to detecting theoperational status of the turbine, one directed at identifying aprobability of hitting a production goal, and one directed atdetermining if the operation of the turbine is meeting a fuel efficiencygoal. Each of these data collectors may be populated with differentsensors or data from different sensors (e.g., a vibration transducer toindicate operational status, a flow meter to indicate production goal,and a fuel gauge to indicate a fuel efficiency) whose output data areindicative of an aspect of a particular goal. Where a single sensor or aset of sensors is helpful for more than one goal, overlapping datacollectors (having some sensors in common and other sensors not incommon) may take input from that sensor or set of sensors, as managed bythe data collection system. If there are constraints on data collection(such as due to power limitations, storage limitations, bandwidthlimitations, input/output processing capabilities, or the like), a rulemay indicate that one goal (e.g., a fuel utilization goal or a pollutionreduction goal that is mandated by law or regulation) takes precedence,such that the data collection for the data collectors associated withthat goal are maintained as others are paused or shut down. Managementof prioritization of goals may be hierarchical or may occur by machinelearning. The data collection system may be seeded with models, or maynot be seeded at all, in iterating towards a predicted state (e.g.,meeting a goal) given the current data it has acquired. In this example,during operation of the turbine the plant owner may decide to bias thesystem towards fuel efficiency. All of the data collectors may still bemonitored, but as the self-organization functionality iterates andpredicts that the system will not collect or is not collecting datasufficient to determine whether the system is or is not meeting aparticular goal, the data collection system may recommend or implementchanges directed at collecting the appropriate data. Further, the plantowner may structure the system with a bias towards a particular goalsuch that the recommended changes to data collection parametersaffecting such goal are made in favor of making other recommendedchanges.

In embodiments, the data collection system may continue iterating in adeep-learning fashion to arrive at a distribution of data collectors,after being seeded with more than one data collection data type, thatoptimizes meeting more than one goal. For example, there may be multiplegoals tracked for a refining environment, such as refining efficiencyand economic efficiency. Refining efficiency for the refining system maybe expressed by comparing fuel put into the system, which can beobtained by knowing the amount of and quality of the fuel being used,and the amount of the refined product output from the system, which iscalculated using the flow out of the system. Economic efficiency of therefining system may be expressed as the ratio between costs to run thesystem, including fuel, labor, materials and services, and the refinedproduct output from the system for a period of time. Data used to trackrefining efficiency may include data from a flow meter, quality datapoint(s), and a thermometer, and data used to track economic efficiencymay be a flow of product output from the system and costs data. Thesedata may be used in the data collection system to predict states;however, the self-organization functionality of the system may iteratetowards a data collection strategy that is optimized to predict statesrelated to both thermal and economic efficiency. The new data collectionschema may include data used previously in the individual datacollectors but may also use new data from different sensors or datasources.

The iteration of the data collection system may be governed by rules, insome embodiments. For example, the data collection system may bestructured to collect data for seeding at a pre-determined frequency.The data collection system may be structured to iterate at least anumber of times, such as when a new component/equipment/fuel source isadded, when a sensor goes off-line, or as standard practice. Forexample, when a sensor measuring the rotation of a boring tool in anoffshore drilling operation goes off-line and the data collection systembegins acquiring data from a new sensor or data collector measuring thesame data points, the data collection system may be structured toiterate for a number of times before the state is utilized in or allowedto affect any downstream actions. The data collection system may bestructured to train off-line or train in situ/online. The datacollection system may be structured to include static and/or manuallyinput data in its data collectors. For example, a data collection systemassociated with such a boring tool may be structured to iterate towardspredicting a distance bored based on a duration of operation. Inembodiments, the data collector(s) include data regarding the speed ofthe boring tools, a distance sensor, a temperature sensor, and the like.

In embodiments, the data collection system may be overruled. Inembodiments, the data collection system may revert to prior settings,such as in the event the self-organization functionality fails, such asif the collected data is insufficient or inappropriately collected, ifuncertainty is too high in a model-based system, if the system is unableto resolve conflicting rules in rule-based system, or the system cannotconverge on a solution in any of the foregoing. For example, sensor dataon a power generation system used by the data collection system mayindicate a non-operational state (such as a seized turbine), but outputsensors and visual inspection, such as by a drone, may indicate normaloperation. In this event, the data collection system may revert to anoriginal data collection schema for seeding the self-organizationfunctionality. In another example, one or more point sensors on arefrigeration system may indicate imminent failure in a compressor, butthe data collector self-organized to collect data associated towardsdetermining a performance metric did not identify the failure. In thisevent, the data collector(s) will revert to an original setting or aversion of the data collector setting that would have also identifiedthe imminent failure of the compressor.

In embodiments, the data collection system may change data collectorsettings in the event that a new component is added that makes thesystem closer to a different system. For example, a vacuum distillationunit is added to an oil and gas refinery to distill naphthalene, but thecurrent data collector settings for the data collection system arederived from a refinery that distills kerosene. In this example, a datastructure with data collector settings for various systems may besearched for a system that is more closely matched to the currentsystem. When a new system is identified as more closely matched, such asone that also distill naphthalene, the new data collector settings(which sensors to use, where to direct them, how frequently to sample,what types of data and points are needed, etc. as described herein) areused to seed the data collection system to iterate towards predicting astate for the system. In embodiments, the data collection system maychange data collector settings in the event that a new set of data isavailable from a third party library. For example, a power generationplant may have optimized a specific turbine model to operate in a highlyefficient way and deposited the data collector settings in a datastructure. The data structure may be continuously scanned for new datacollectors that better aid in monitoring power generation and thus,result in optimizing the operation of the turbine.

In embodiments, the data collection system may utilize self-organizationfunctionality to uncover unknown variables. For example, the datacollection system may iterate to identify a missing variable to be usedfor further iterations. For example, an under-utilized tank in a legacycondensate/make-up water system of a power station may have an unknowncapacity because it is inaccessible and no documentation exists on thetank. Various aspects of the tank may be measured by a swarm of datacollectors to arrive at an estimated volume (e.g., flow into adownstream space, duration of a dye traced solution to work through thesystem), which can then be fed into the data collection system as a newvariable.

In embodiments, the data collection system node may be on a machine, ona data collector (or a group of them), in a network infrastructure(enterprise or other), or in the cloud. In embodiments, there may bedistributed neurons across nodes (e.g., machine, data collector,network, cloud).

In an aspect, and as illustrated in FIG. 164, the data collection system12004 can be arranged to collect data in an industrial environment12000, e.g., from one or more targets 12002. In the illustratedembodiments, the data collection system 12004 includes a group or“swarm” 12006 of the data collectors 12008, a network 12010, a computingsystem 12012, and a database or the data pool 12014. Each of the datacollectors 12008 can include one or more input sensors and becommunicatively coupled to any and all of the other components of thedata collection system 12004, as is partially illustrated by theconnecting arrows between components.

The targets 12002 can be any form of machinery or component thereof inthe industrial environment 12000. Examples of such industrialenvironments 12000 include but are not limited to factories, pipelines,construction sites, ocean oil rigs, ships, airplanes or other aircraft,mining environments, drilling environments, refineries, distributionenvironments, manufacturing environments, energy source extractionenvironments, offshore exploration sites, underwater exploration sites,assembly lines, warehouses, power generation environments, and hazardouswaste environments, each of which may include one or more of the targets12002. The targets 12002 can take any form of item or location at whicha sensor can obtain data. Examples of such targets 12002 include but arenot limited to machines, pipelines, equipment, installations, tools,vehicles, turbines, speakers, lasers, automatons, computer equipment,industrial equipment, and switches.

The self-organization functionality of the data collection system 12004can be performed at or by any of the components of the data collectionsystem 12004. In embodiments, the data collector 12008 or the swarm12006 of the data collectors 12008 can self-organize without assistancefrom other components and based on, e.g., the data sensed by itsassociated sensors and other knowledge. In embodiments, the network12010 can self-organize without assistance from other components andbased on, e.g., the data sensed by the data collectors 12008 or otherknowledge. Similarly, the computing system 12012 and/or the data pool12014 without assistance from other components and based on, e.g., thedata sensed by the data collectors 12008 or other knowledge. It shouldbe appreciated that any combination or hybrid-type self-organizationsystem can also be implemented.

In many examples, the data collection system 12004 can perform or enablevarious methods or systems for data collection having self-organizationfunctionality in the industrial environment 12000. These methods andsystems can include analyzing a plurality of sensor inputs, e.g.,received from or sensed by sensors at the data collector(s) 12008. Themethods and systems can also include sampling the received data andself-organizing at least one of: (i) a storage operation of the data;(ii) a collection operation of sensors that provide the plurality ofsensor inputs, and (iii) a selection operation of the plurality ofsensor inputs.

In aspects, the storage operation can include storing the data in alocal database, e.g., of the data collector 12008, the computing system12012, and/or the data pool 12014. The data can also be summarized overa given time period to reduce a size of the sensed data. The summarizeddata can be sent to one or more data acquisition boxes, to one or moredata centers, and/or to other components of the system or other,separate systems. Summarizing the data over a given time period toreduce the size of the data, in some aspects, can include determining aspeed at which data can be sent via a network (e.g., the network 12010).In embodiments, the size of the summarized data corresponds to the speedat which data can be sent continuously in real time via the network. Insuch aspects, or others, the summarized data can be continuously sent,e.g., to an external device via the network.

In various implementations, the methods and systems can includecommitting the summarized data to a local ledger, identifying one ormore other accessible signal acquisition instruments on an accessiblenetwork, and/or synchronizing the summarized data at the local ledgerwith at least one of the other accessible signal acquisition instruments(e.g., the data collectors 12008). In embodiments, receiving a remotestream of sensor data from one or more other accessible signalacquisition instruments via a network can be included. An advertisementmessage to a potential client indicating availability of at least one ofthe locally stored data, the summarized data, and the remote stream ofsensor data can also or alternatively be sent.

The methods and systems can include identifying one or more otheraccessible signal acquisition instruments (e.g., the data collectors12008) on an accessible network (e.g., the network 12010), nominating atleast one of the one or more other accessible signal acquisitioninstruments as a logical communication hub, and providing the logicalcommunication hub with a list of available data and their associatedsources. The list of available data and their associated sources can beprovided to the logical communication hub utilizing a hybridpeer-to-peer communications protocol.

In some aspects, the storage operation can include storing the data in alocal database and automatically organizing at least one parameter ofthe data pool utilizing machine learning. The organizing can be based atleast in part on receiving information regarding at least one of anaccuracy of classification and an accuracy of prediction of an externalmachine learning system that uses data from the data pool (e.g., thedata pool 12014).

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs and self-organizing at least oneof: (i) a storage operation of the data; (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs.

The present disclosure describes a system for data collection in anindustrial environment having self-organization functionality, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the industrial environment and forgenerating data associated with the plurality of sensor inputs, and aself-organizing system for self-organizing at least one of (i) a storageoperation of the data, (ii) a data collection operation of sensors thatprovide the plurality of sensor inputs, and (iii) a selection operationof the plurality of sensor inputs.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs; and self-organizing at least oneof: (i) a storage operation of the data; (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs. In embodiments,the storage operation includes storing the data in a local database, andsummarizing the data over a given time period to reduce a size of thedata.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the method further includessending the summarized data to one or more data acquisition boxes.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the method further includessending the summarized data to one or more data centers.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein summarizing the data over agiven time period to reduce the size of the data includes determining aspeed at which data can be sent via a network. In embodiments, the sizeof the summarized data corresponds to the speed at which data can besent continuously in real time via the network.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the method further includescontinuously sending the summarized data to an external device via thenetwork.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs and self-organizing at least oneof: (i) a storage operation of the data; (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs. In embodiments,the storage operation includes storing the data in a local database,summarizing the data over a given time period to reduce a size of thedata, committing the summarized data to a local ledger, identifying oneor more other accessible signal acquisition instruments on an accessiblenetwork, and synchronizing the summarized data at the local ledger withat least one of the other accessible signal acquisition instruments. Afurther embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the method further includesreceiving a remote stream of sensor data from one or more otheraccessible signal acquisition instruments via a network.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the method further includessending an advertisement message to a potential client indicatingavailability of at least one of the locally stored data, the summarizeddata, and the remote stream of sensor data.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs; samplingdata received from the sensor inputs, self-organizing at least one of:(i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs. In embodiments,the storage operation includes storing the data in a local database, andsummarizing the data over a given time period to reduce a size of thedata, identifying one or more other accessible signal acquisitioninstruments on an accessible network, nominating at least one of the oneor more other accessible signal acquisition instruments as a logicalcommunication hub, and providing the logical communication hub with alist of available data and their associated sources.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the list of available data andtheir associated sources is provided to the logical communication hubutilizing a hybrid peer-to-peer communications protocol. The presentdisclosure describes a method for data collection in an industrialenvironment having self-organization functionality, the method accordingto one disclosed non-limiting embodiment of the present disclosure caninclude analyzing a plurality of sensor inputs, sampling data receivedfrom the sensor inputs, and self-organizing at least one of (i) astorage operation of the data, (ii) a collection operation of sensorsthat provide the plurality of sensor inputs, and (iii) a selectionoperation of the plurality of sensor inputs. In embodiments, the storageoperation includes storing the data in a local database, summarizing thedata over a given time period to reduce a size of the data, storing thedata in a local database, and automatically organizing at least oneparameter of the database utilizing machine learning. In embodiments,the organizing is based at least in part on receiving informationregarding at least one of an accuracy of classification and an accuracyof prediction of an external machine learning system that uses data fromthe database.

In aspects, the collection operation of sensors that provide theplurality of sensor inputs can include receiving instructions directinga mobile data collector unit (e.g., the data collector 12008) to operatesensors at a target (e.g., 12002). In embodiments, at least one of theplurality of sensors is arranged in the mobile data collector unit. Acommunication can be transmitted to one or more other mobile datacollector units (12008) regarding the instructions. The swarm 12006 orportion thereof can self-organize a distribution of the mobile datacollector unit and the one or more other mobile data collector units(e.g., the data collectors 12008) at the target 12002.

In aspects, self-organizing the distribution of the mobile datacollector units at the target 12002 comprises utilizing a machinelearning algorithm to determine a respective target location for each ofthe mobile data collector units. The machine learning algorithm canutilize one or more of a plurality of features to determine therespective target locations. Examples of the features can include:battery life of the mobile data collector units (the data collectors12008), a type of the target 12002 being sensed, a type of signal beingsensed, a size of the target 12002, a number of mobile data collectorunits (the data collectors 12008) needed to cover the target 12002, anumber of data points needed for the target 12002, a success in prioraccomplishment of signal capture, information received from aheadquarters or other components from which the instructions arereceived, and historical information regarding the sensors operated atthe target 12002.

In implementations, self-organizing the distribution of the mobile datacollector unit and the one or more other mobile data collector units atthe target location can include proposing a target location for themobile data collector unit(s), transmitting the target location to atleast one other mobile data collector units, receiving confirmation thatthere is no contention for the target location, directing one of themobile data collector units to the target location, and collectingsensor data at the target location from the directed mobile datacollector unit.

Self-organizing the distribution of the mobile data collector unit andthe one or more other mobile data collector units at the target locationcan also include, in certain embodiments, proposing a target locationfor the mobile data collector unit, transmitting the target location toat least one of the one or more other mobile data collector units,receiving a proposal for a new target location, directing the mobiledata collector unit to the new target location, and collecting sensordata at the new target location from the mobile data collector unit.

In additional or alternative aspects, self-organizing the distributionof the mobile data collector unit and the one or more other mobile datacollector units at the target location can comprise proposing a targetlocation for the mobile data collector unit, determining that at leastone of the one or more other mobile data collector units is at or movingto the target location, determining a new target location based on theat least one of the one or more other mobile data collector units beingat or moving to the target location, directing the mobile data collectorunit to the new target location, and collecting sensor data at the newtarget location from the mobile data collector unit.

Self-organizing the distribution of the mobile data collector unit andthe one or more other mobile data collector units at the target locationcan further comprise determining a type of the sensors to operate at thetarget 12002, receiving confirmation that there is no contention for thetype of sensors, directing the mobile data collector unit to operate thetype of sensors at the target 12002, and collecting sensor data from thetype of sensors at the target 12002 from the mobile data collector unit.

In aspects, self-organizing the distribution of the mobile datacollector unit and the one or more other mobile data collector units atthe target location can include determining a type of the sensors tooperate at the target, transmitting the type of the sensors to at leastone of the one or more other mobile data collector units, receiving aproposal for a new type of the sensors, directing the mobile datacollector unit to operate the new type of sensors at the target, andcollecting sensor data from the new type of sensors at the target fromthe mobile data collector unit.

Self-organizing the distribution of the mobile data collector unit andthe one or more other mobile data collector units at the target locationcan include determining a type of the sensors to operate at the target,determining that at least one of the one or more other mobile datacollector units is operating or can operate the type of the sensors atthe target, determining a new type of the sensors based on the at leastone of the one or more other mobile data collector units operating orbeing capable of operating the type of the sensors at the target,directing the mobile data collector unit to operate the new type ofsensors at the target, and collecting sensor data from the new type ofsensors at the target from the mobile data collector unit.

Self-organizing the distribution of the mobile data collector unit andthe one or more other mobile data collector units at the targetlocation, in some implementations, can comprise utilizing a swarmoptimization algorithm to allocate areas of sensor responsibilityamongst the mobile data collector unit and the one or more other mobiledata collector units. Examples of the swarm optimization algorithminclude but are not limited to Genetic Algorithms (GA), Ant ColonyOptimization (ACO), Particle Swarm Optimization (PSO), DifferentialEvolution (DE), Artificial Bee Colony (ABC), Glowworm Swarm Optimization(GSO), and Cuckoo Search Algorithm (CSA), Genetic Programming (GP),Evolution Strategy (ES), Evolutionary Programming (EP), FireflyAlgorithm (FA), Bat Algorithm (BA) and Grey Wolf Optimizer (GWO), orcombinations thereof.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs.

The present disclosure describes a system for data collection in anindustrial environment having automated self-organization, the systemaccording to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the industrial environment and forgenerating data associated with the plurality of sensor inputs, and aself-organizing system for self-organizing at least one of (i) a storageoperation of the data, (ii) a data collection operation of sensors thatprovide the plurality of sensor inputs, and (iii) a selection operationof the plurality of sensor inputs.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs; samplingdata received from the sensor inputs and self-organizing at least one of(i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs. In embodiments,the collection operation of sensors that provide the plurality of sensorinputs includes receiving instructions directing a mobile data collectorunit to operate sensors at a target. In embodiments, at least one of theplurality of sensors is arranged in the mobile data collector unit,transmitting a communication to one or more other mobile data collectorunits regarding the instructions, and self-organizing a distribution ofthe mobile data collector unit and the one or more other mobile datacollector units at the target.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target includes utilizing a machinelearning algorithm to determine a respective target location for each ofthe mobile data collector units.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the machine learning algorithmutilizes one or more of a plurality of features to determine therespective target locations, the plurality of features including:battery life of the mobile data collector units, a type of the targetbeing sensed, a type of signal being sensed, a size of the target, anumber of mobile data collector units needed to cover the target, anumber of data points needed for the target, a success in prioraccomplishment of signal capture, information received from aheadquarters from which the instructions are received, and historicalinformation regarding the sensors operated at the target.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target location includes proposing atarget location for the mobile data collector unit, transmitting thetarget location to at least one of the one or more other mobile datacollector units, receiving confirmation that there is no contention forthe target location, directing the mobile data collector unit to thetarget location, and collecting sensor data at the target location fromthe mobile data collector unit.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target location includes proposing atarget location for the mobile data collector unit, transmitting thetarget location to at least one of the one or more other mobile datacollector units, receiving a proposal for a new target location,directing the mobile data collector unit to the new target location andcollecting sensor data at the new target location from the mobile datacollector unit.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target location includes proposing atarget location for the mobile data collector unit, determining that atleast one of the one or more other mobile data collector units is at ormoving to the target location, determining a new target location basedon the at least one of the one or more other mobile data collector unitsbeing at or moving to the target location, directing the mobile datacollector unit to the new target location and collecting sensor data atthe new target location from the mobile data collector unit.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target location includes determininga type of the sensors to operate at the target, receiving confirmationthat there is no contention for the type of sensors, directing themobile data collector unit to operate the type of sensors at the target,and collecting sensor data from the type of sensors at the target fromthe mobile data collector unit.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs. The collectionoperation of sensors that provide the plurality of sensor inputsincludes receiving instructions directing a mobile data collector unitto operate sensors at a target. In embodiments, at least one of theplurality of sensors is arranged in the mobile data collector unit,transmitting a communication to one or more other mobile data collectorunits regarding the instructions, self-organizing a distribution of themobile data collector unit and the one or more other mobile datacollector units at the target. In embodiments, self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target location includes determininga type of the sensors to operate at the target, transmitting the type ofthe sensors to at least one of the one or more other mobile datacollector units, receiving a proposal for a new type of the sensors,directing the mobile data collector unit to operate the new type ofsensors at the target and collecting sensor data from the new type ofsensors at the target from the mobile data collector unit.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target location includes determininga type of the sensors to operate at the target, determining that atleast one of the one or more other mobile data collector units isoperating or can operate the type of the sensors at the target,determining a new type of the sensors based on the at least one of theone or more other mobile data collector units operating or being capableof operating the type of the sensors at the target, directing the mobiledata collector unit to operate the new type of sensors at the target,and collecting sensor data from the new type of sensors at the targetfrom the mobile data collector unit.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target location includes utilizing aswarm optimization algorithm to allocate areas of sensor responsibilityamongst the mobile data collector unit and the one or more other mobiledata collector units.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the swarm optimizationalgorithm is one or more types of Genetic Algorithms (GA), Ant ColonyOptimization (ACO), Particle Swarm Optimization (PSO), DifferentialEvolution (DE), Artificial Bee Colony (ABC), Glowworm Swarm Optimization(GSO), and Cuckoo Search Algorithm (CSA), Genetic Programming (GP),Evolution Strategy (ES), Evolutionary Programming (EP), FireflyAlgorithm (FA), Bat Algorithm (BA) and Grey Wolf Optimizer (GWO).

In aspects, the selection operation can comprise receiving a signalrelating to at least one condition of the industrial environment 12000and, based on the signal, changing at least one of the sensor inputsanalyzed and a frequency of the sampling. The at least one condition ofthe industrial environment can be a signal-to-noise ratio of the sampleddata. The selection operation can include identifying a target signal tobe sensed. Additionally, the selection operation further can includeidentifying one or more non-target signals in a same frequency band asthe target signal to be sensed and, based on the identified one or morenon-target signals, changing at least one of the sensor inputs analyzedand a frequency of the sampling.

The selection operation can comprise identifying other data collectorssensing in a same signal band as the target signal to be sensed, and,based on the identified other data collectors, changing at least one ofthe sensor inputs analyzed and a frequency of the sampling. Inimplementations, the selection operation can further compriseidentifying a level of activity of a target associated with the targetsignal to be sensed and, based on the identified level of activity,changing at least one of the sensor inputs analyzed and a frequency ofthe sampling.

The selection operation can further comprise receiving data indicativeof environmental conditions near a target associated with the targetsignal, comparing the received environmental conditions of the targetwith past environmental conditions near the target or another targetsimilar to the target, and, based on the comparison, changing at leastone of the sensor inputs analyzed and a frequency of the sampling. Atleast a portion of the received sampling data can be transmitted toanother data collector according to a predetermined hierarchy of datacollection.

The selection operation further comprises, in some aspects, receivingdata indicative of environmental conditions near a target associatedwith the target signal, transmitting at least a portion of the receivedsampling data to another data collector according to a predeterminedhierarchy of data collection, receiving feedback via a networkconnection relating to a quality or sufficiency of the transmitted data,analyzing the received feedback, and, based on the analysis of thereceived feedback, changing at least one of the sensor inputs analyzed,the frequency of sampling, the data stored, and the data transmitted.

Additionally, or alternatively, the selection operation can comprisereceiving data indicative of environmental conditions near a targetassociated with the target signal, transmitting at least a portion ofthe received sampling data to another data collector according to apredetermined hierarchy of data collection, receiving feedback via anetwork connection relating to one or more yield metrics of thetransmitted data, analyzing the received feedback, and, based on theanalysis of the received feedback, changing at least one of the sensorinputs analyzed, the frequency of sampling, the data stored, and thedata transmitted.

In implementations, the selection operation can include receiving dataindicative of environmental conditions near a target associated with thetarget signal, transmitting at least a portion of the received samplingdata to another data collector according to a predetermined hierarchy ofdata collection, receiving feedback via a network connection relating topower utilization, analyzing the received feedback, and based on theanalysis of the received feedback, changing at least one of the sensorinputs analyzed, the frequency of sampling, the data stored, and thedata transmitted.

The selection operation can also or alternatively comprise receivingdata indicative of environmental conditions near a target associatedwith the target signal, transmitting at least a portion of the receivedsampling data to another data collector according to a predeterminedhierarchy of data collection, receiving feedback via a networkconnection relating to a quality or sufficiency of the transmitted data,analyzing the received feedback, and, based on the analysis of thereceived feedback, executing a dimensionality reduction algorithm on thesensed data. The dimensionality reduction algorithm can be one or moreof a Decision Tree, Random Forest, Principal Component Analysis, FactorAnalysis, Linear Discriminant Analysis, Identification based oncorrelation matrix, Missing Values Ratio, Low Variance Filter, RandomProjections, Nonnegative Matrix Factorization, Stacked Auto-encoders,Chi-square or Information Gain, Multidimensional Scaling, CorrespondenceAnalysis, Factor Analysis, Clustering, and Bayesian Models. Thedimensionality reduction algorithm can be performed at the datacollector 12008, the swarm 12006 of the data collectors 12008, thenetwork 12010, the computing system 12012, the data pool 12014, orcombination thereof. In aspects, executing the dimensionality reductionalgorithm can comprise sending the sensed data to a remote computingdevice.

In aspects, a system for self-organizing collection and storage of datacollection in a power generation environment can include a datacollector for handling a plurality of sensor inputs from varioussensors. Such sensors can be a component of the data collector, externalto the data collector (e.g., external sensors or components of differentdata collector(s)), or a combination thereof. The plurality of sensorinputs can be configured to sense at least one of an operational mode, afault mode, and a health status of at least one target system. Examplesof such target systems include but are not limited to a fuel handlingsystem, a power source, a turbine, a generator, a gear system, anelectrical transmission system, a transformer, a fuel cell, and anenergy storage device/system. The system can also include aself-organizing system that can be configured for self-organizing atleast one of: (i) a storage operation of the data, (ii) a datacollection operation of the sensors that provide the plurality of sensorinputs, and (iii) a selection operation of the plurality of sensorinput, as is described herein.

In aspects, the system can include the swarm 12006 of mobile datacollectors (e.g., the data collectors 12008). Further, in additional oralternative aspects, the self-organizing system can generate, iterate,optimize, etc. a storage specification for organizing storage of thedata. The storage specification, e.g., can specify which data will bestored for local storage in the power generation environment, and whichdata will be output for streaming via a network connection (e.g., thenetwork 12010) from the power generation environment. Other datacollection, generation, and/or storage operations can be performed orenabled by the system, as is described herein.

In a non-limiting example, the system can include a plurality of sensorsconfigured to sense various parameters in the environment of a turbineas a target system. Vibration sensors, temperature sensors, acousticsensors, strain gauges, and accelerometers, and the like may be utilizedby the system to generate data regarding the operation of the turbine.As mentioned herein, any and all of the storage operation, the datacollection operation, and the selection operation of the plurality ofsensor inputs may be adapted, optimized, learned, or otherwiseself-organized by the system.

In aspects, a system for self-organizing collection and storage of datacollection in energy source extraction environment can include a datacollector for handling a plurality of sensor inputs from varioussensors. Examples of such energy source extraction environments includea coal mining environment, a metal mining environment, a mineral miningenvironment, and an oil drilling environment, although other extractionenvironments are contemplated by the present disclosure. The sensorsutilized can be a component of the data collector, external to the datacollector (e.g., external sensors or components of different datacollector(s)), or a combination thereof. The plurality of sensor inputscan be configured to sense at least one of an operational mode, a faultmode, and a health status of at least one target system. Examples ofsuch target systems include but are not limited to a hauling system, alifting system, a drilling system, a mining system, a digging system, aboring system, a material handling system, a conveyor system, a pipelinesystem, a wastewater treatment system, and a fluid pumping system.

The system can also include a self-organizing system that can beconfigured for self-organizing at least one of: (i) a storage operationof the data, (ii) a data collection operation of the sensors thatprovide the plurality of sensor inputs, and (iii) a selection operationof the plurality of sensor input, as is described herein. In aspects,the system can include the swarm 12006 of mobile data collectors (e.g.,the data collectors 12008). Further, in additional or alternativeaspects, the self-organizing system can generate, iterate, optimize,etc. a storage specification for organizing storage of the data. Thestorage specification, e.g., can specify which data will be stored forlocal storage in the power generation environment, and which data willbe output for streaming via a network connection (e.g., the network12010) from the power generation environment. Other data collection,generation, and/or storage operations can be performed or enabled by thesystem, as is described herein.

In a non-limiting example, the system can include a plurality of sensorsconfigured to sense various parameters in the environment of a fluidpumping system as a target system. Vibration sensors, flow sensors,pressure sensors, temperature sensors, acoustic sensors, and the likemay be utilized by the system to generate data regarding the operationof the fluid pumping system. As mentioned herein, any and all of thestorage operation, the data collection operation, and the selectionoperation of the plurality of sensor inputs may be adapted, optimized,learned, or otherwise self-organized by the system.

In implementations, a system for self-organizing collection and storageof data collection in a manufacturing environment can include a datacollector for handling a plurality of sensor inputs from varioussensors. Such sensors can be a component of the data collector, externalto the data collector (e.g., external sensors or components of differentdata collector(s)), or a combination thereof. The plurality of sensorinputs can be configured to sense at least one of an operational mode, afault mode, and a health status of at least one target system. Examplesof such target systems include but are not limited to a power system, aconveyor system, a generator, an assembly line system, a wafer handlingsystem, a chemical vapor deposition system, an etching system, aprinting system, a robotic handling system, a component assembly system,an inspection system, a robotic assembly system, and a semi-conductorproduction system. The system can also include a self-organizing systemthat can be configured for self-organizing at least one of: (i) astorage operation of the data, (ii) a data collection operation of thesensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor input, as is describedherein.

In aspects, the system can include the swarm 12006 of mobile datacollectors (e.g., the data collectors 12008). Further, in additional oralternative aspects, the self-organizing system can generate, iterate,optimize, etc. a storage specification for organizing storage of thedata. The storage specification, e.g., can specify which data will bestored for local storage in the power generation environment, and whichdata will be output for streaming via a network connection (e.g., thenetwork 12010) from the power generation environment. Other datacollection, generation, and/or storage operations can be performed orenabled by the system, as is described herein.

In a non-limiting example, the system can include a plurality of sensorsconfigured to sense various parameters in the environment of a waferhandling system as a target system. Vibration sensors, fluid flowsensors, pressure sensors, gas sensors, temperature sensors, and thelike may be utilized by the system to generate data regarding theoperation of the wafer handling system. As mentioned herein, any and allof the storage operation, the data collection operation, and theselection operation of the plurality of sensor inputs may be adapted,optimized, learned, or otherwise self-organized by the system.

Also disclosed are embodiments of an additional or alternative systemfor self-organizing collection and storage of data collection inrefining environment. Such system(s) can include a data collector forhandling a plurality of sensor inputs from various sensors. Examples ofsuch refining environments include a chemical refining environment, apharmaceutical refining environment, a biological refining environment,and a hydrocarbon refining environment, although other refiningenvironments are contemplated by the present disclosure. The sensorsutilized can be a component of the data collector, external to the datacollector (e.g., external sensors or components of different datacollector(s)), or a combination thereof. The plurality of sensor inputscan be configured to sense at least one of an operational mode, a faultmode, and a health status of at least one target system. Examples ofsuch target systems include but are not limited to a power system, apumping system, a mixing system, a reaction system, a distillationsystem, a fluid handling system, a heating system, a cooling system, anevaporation system, a catalytic system, a moving system, and a containersystem.

The system can also include a self-organizing system that can beconfigured for self-organizing at least one of: (i) a storage operationof the data, (ii) a data collection operation of the sensors thatprovide the plurality of sensor inputs, and (iii) a selection operationof the plurality of sensor input, as is described herein. In aspects,the system can include the swarm 12006 of mobile data collectors (e.g.,the data collectors 12008). Further, in additional or alternativeaspects, the self-organizing system can generate, iterate, optimize,etc. a storage specification for organizing storage of the data. Thestorage specification, e.g., can specify which data will be stored forlocal storage in the power generation environment, and which data willbe output for streaming via a network connection (e.g., the network12010) from the power generation environment. Other data collection,generation, and/or storage operations can be performed or enabled by thesystem, as is described herein.

In a non-limiting example, the system can include a plurality of sensorsconfigured to sense various parameters in the refining environment of aheating system as a target system. Temperature sensors, fluid flowsensors, pressure sensors, and the like may be utilized by the system togenerate data regarding the operation of the heating system. Asmentioned herein, any and all of the storage operation, the datacollection operation, and the selection operation of the plurality ofsensor inputs may be adapted, optimized, learned, or otherwiseself-organized by the system.

In aspects, a system for self-organizing collection and storage of datacollection in a distribution environment can include a data collectorfor handling a plurality of sensor inputs from various sensors. Suchsensors can be a component of the data collector, external to the datacollector (e.g., external sensors or components of different datacollector(s)), or a combination thereof. The plurality of sensor inputscan be configured to sense at least one of an operational mode, a faultmode, and a health status of at least one target system. Examples ofsuch target systems include but are not limited to a power system, aconveyor system, a robotic transport system, a robotic handling system,a packing system, a cold storage system, a hot storage system, arefrigeration system, a vacuum system, a hauling system, a liftingsystem, an inspection system, and a suspension system. The system canalso include a self-organizing system that can be configured forself-organizing at least one of: (i) a storage operation of the data,(ii) a data collection operation of the sensors that provide theplurality of sensor inputs, and (iii) a selection operation of theplurality of sensor input, as is described herein.

In aspects, the system can include the swarm 12006 of mobile datacollectors (e.g., the data collectors 12008). Further, in additional oralternative aspects, the self-organizing system can generate, iterate,optimize, etc. a storage specification for organizing storage of thedata. The storage specification, e.g., can specify which data will bestored for local storage in the power generation environment, and whichdata will be output for streaming via a network connection (e.g., thenetwork 12010) from the power generation environment. Other datacollection, generation, and/or storage operations can be performed orenabled by the system, as is described herein.

In a non-limiting example, the system can include a plurality of sensorsconfigured to sense various parameters in the distribution environmentof a refrigeration system as a target system. Power sensors, temperaturesensors, vibration sensors, strain gauges, and the like may be utilizedby the system to generate data regarding the operation of the turbine.As mentioned herein, any and all of the storage operation, the datacollection operation, and the selection operation of the plurality ofsensor inputs may be adapted, optimized, learned, or otherwiseself-organized by the system.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs.

The present disclosure describes a system for data collection in anindustrial environment having automated self-organization, the systemaccording to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the industrial environment and forgenerating data associated with the plurality of sensor inputs, and aself-organizing system for self-organizing at least one of (i) a storageoperation of the data, (ii) a data collection operation of sensors thatprovide the plurality of sensor inputs, and (iii) a selection operationof the plurality of sensor inputs.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs. In embodiments,the selection operation includes receiving a signal relating to at leastone condition of the industrial environment, based on the signal,changing at least one of the sensor inputs analyzed and a frequency ofthe sampling.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one condition ofthe industrial environment is a signal-to-noise ratio of the sampleddata.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the selection operationincludes identifying a target signal to be sensed.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the selection operationfurther includes identifying one or more non-target signals in a samefrequency band as the target signal to be sensed, and based on theidentified one or more non-target signals, changing at least one of thesensor inputs analyzed and a frequency of the sampling.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the selection operationfurther includes identifying other data collectors sensing in a samesignal band as the target signal to be sensed, and based on theidentified other data collectors, changing at least one of the sensorinputs analyzed and a frequency of the sampling.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the selection operationfurther includes identifying a level of activity of a target associatedwith the target signal to be sensed, and based on the identified levelof activity, changing at least one of the sensor inputs analyzed and afrequency of the sampling.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the selection operationfurther includes receiving data indicative of environmental conditionsnear a target associated with the target signal, comparing the receivedenvironmental conditions of the target with past environmentalconditions near the target or another target similar to the target, andbased on the comparison, changing at least one of the sensor inputsanalyzed and a frequency of the sampling.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the selection operationfurther includes transmitting at least a portion of the receivedsampling data to another data collector according to a predeterminedhierarchy of data collection.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs. In embodiments,the selection operation includes identifying a target signal to besensed, receiving a signal relating to at least one condition of theindustrial environment, based on the signal, changing at least one ofthe sensor inputs analyzed and a frequency of the sampling, receivingdata indicative of environmental conditions near a target associatedwith the target signal, transmitting at least a portion of the receivedsampling data to another data collector according to a predeterminedhierarchy of data collection, receiving feedback via a networkconnection relating to a quality or sufficiency of the transmitted data,analyzing the received feedback, and based on the analysis of thereceived feedback, changing at least one of the sensor inputs analyzed,the frequency of sampling, the data stored, and the data transmitted.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs. In embodiments,the selection operation includes identifying a target signal to besensed, receiving a signal relating to at least one condition of theindustrial environment, based on the signal, changing at least one ofthe sensor inputs analyzed and a frequency of the sampling, receivingdata indicative of environmental conditions near a target associatedwith the target signal, transmitting at least a portion of the receivedsampling data to another data collector according to a predeterminedhierarchy of data collection, receiving feedback via a networkconnection relating to one or more yield metrics of the transmitteddata, analyzing the received feedback, and based on the analysis of thereceived feedback, changing at least one of the sensor inputs analyzed,the frequency of sampling, the data stored, and the data transmitted.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs. In embodiments,the selection operation includes identifying a target signal to besensed, receiving a signal relating to at least one condition of theindustrial environment, based on the signal, changing at least one ofthe sensor inputs analyzed and a frequency of the sampling, receivingdata indicative of environmental conditions near a target associatedwith the target signal, transmitting at least a portion of the receivedsampling data to another data collector according to a predeterminedhierarchy of data collection, receiving feedback, via a networkconnection relating to power utilization, analyzing the receivedfeedback, and based on the analysis of the received feedback, changingat least one of the sensor inputs analyzed, the frequency of sampling,the data stored, and the data transmitted.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs. In embodiments,the selection operation includes identifying a target signal to besensed, receiving a signal relating to at least one condition of theindustrial environment, based on the signal, changing at least one ofthe sensor inputs analyzed and a frequency of the sampling, receivingdata indicative of environmental conditions near a target associatedwith the target signal, transmitting at least a portion of the receivedsampling data to another data collector according to a predeterminedhierarchy of data collection, receiving feedback via a networkconnection relating to a quality or sufficiency of the transmitted data,analyzing the received feedback, and based on the analysis of thereceived feedback, executing a dimensionality reduction algorithm on thesensed data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the dimensionality reductionalgorithm is one or more of a Decision Tree, Random Forest, PrincipalComponent Analysis, Factor Analysis, Linear Discriminant Analysis,Identification based on correlation matrix, Missing Values Ratio, LowVariance Filter, Random Projections, Nonnegative Matrix Factorization,Stacked Auto-encoders, Chi-square or Information Gain, MultidimensionalScaling, Correspondence Analysis, Factor Analysis, Clustering, andBayesian Models.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the dimensionality reductionalgorithm is performed at a data collector.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein executing the dimensionalityreduction algorithm includes sending the sensed data to a remotecomputing device.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs. In embodiments,the selection operation includes identifying a target signal to besensed, receiving a signal relating to at least one condition of theindustrial environment, based on the signal, changing at least one ofthe sensor inputs analyzed and a frequency of the sampling, receivingdata indicative of environmental conditions near a target associatedwith the target signal, transmitting at least a portion of the receivedsampling data to another data collector according to a predeterminedhierarchy of data collection, receiving feedback via a networkconnection relating to at least one of a bandwidth and a quality or ofthe network connection, analyzing the received feedback, and based onthe analysis of the received feedback, changing at least one of thesensor inputs analyzed, the frequency of sampling, the data stored, andthe data transmitted.

The present disclosure describes a system for self-organizing collectionand storage of data collection in a power generation environment, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the power generation environment. Inembodiments, the plurality of sensor inputs is configured to sense atleast one of an operational mode, a fault mode, and a health status ofat least one target system selected from a group consisting of a fuelhandling system, a power source, a turbine, a generator, a gear system,an electrical transmission system, and a transformer, and aself-organizing system for self-organizing at least one of (i) a storageoperation of the data, (ii) a data collection operation of the sensorsthat provide the plurality of sensor inputs, and (iii) a selectionoperation of the plurality of sensor inputs.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemorganizes a swarm of mobile data collectors to collect data from aplurality of target systems.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemgenerates a storage specification for organizing storage of the data,the storage specification specifying data for local storage in the powergeneration environment and specifying data for streaming via a networkconnection from the power generation environment.

The present disclosure describes a system for self-organizing collectionand storage of data collection in an energy source extractionenvironment, the system according to one disclosed non-limitingembodiment of the present disclosure can include a data collector forhandling a plurality of sensor inputs from sensors in the energyextraction environment. In embodiments, the plurality of sensor inputsis configured to sense at least one of an operational mode, a faultmode, and a health status of at least one target system selected from agroup consisting of a hauling system, a lifting system, a drillingsystem, a mining system, a digging system, a boring system, a materialhandling system, a conveyor system, a pipeline system, a wastewatertreatment system, and a fluid pumping system, and a self-organizingsystem for self-organizing at least one of (i) a storage operation ofthe data, (ii) a data collection operation of the sensors that providethe plurality of sensor inputs, and (iii) a selection operation of theplurality of sensor inputs.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemorganizes a swarm of mobile data collectors to collect data from aplurality of target systems.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemgenerates a storage specification for organizing storage of the data,the storage specification specifying data for local storage in theenergy extraction environment and specifying data for streaming via anetwork connection from the energy extraction environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy source extractionenvironment is a coal mining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy source extractionenvironment is a metal mining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy source extractionenvironment is a mineral mining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy source extractionenvironment is an oil drilling environment.

The present disclosure describes a system for self-organizing collectionand storage of data collection in a manufacturing environment, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the power generation environment. Inembodiments, the plurality of sensor inputs is configured to sense atleast one of an operational mode, a fault mode, and a health status ofat least one target system selected from a group consisting of a powersystem, a conveyor system, a generator, an assembly line system, a waferhandling system, a chemical vapor deposition system, an etching system,a printing system, a robotic handling system, a component assemblysystem, an inspection system, a robotic assembly system, and asemi-conductor production system, and a self-organizing system forself-organizing at least one of (i) a storage operation of the data,(ii) a data collection operation of the sensors that provide theplurality of sensor inputs, and (iii) a selection operation of theplurality of sensor inputs.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemorganizes a swarm of mobile data collectors to collect data from aplurality of target systems.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemgenerates a storage specification for organizing the storage of thedata, the storage specification specifying data for local storage in themanufacturing environment and specifying data for streaming via anetwork connection from the manufacturing environment.

The present disclosure describes a system for self-organizing collectionand storage of data collection in a refining environment, the systemaccording to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the power generation environment. Inembodiments, the plurality of sensor inputs is configured to sense atleast one of an operational mode, a fault mode and a health status of atleast one target system selected from a group consisting of a powersystem, a pumping system, a mixing system, a reaction system, adistillation system, a fluid handling system, a heating system, acooling system, an evaporation system, a catalytic system, a movingsystem, and a container system, and a self-organizing system forself-organizing at least one of (i) a storage operation of the data,(ii) a data collection operation of the sensors that provide theplurality of sensor inputs, and (iii) a selection operation of theplurality of sensor inputs.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemorganizes a swarm of mobile data collectors to collect data from aplurality of target systems.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemgenerates a storage specification for organizing the storage of thedata, the storage specification specifying data for local storage in therefining environment and specifying data for streaming via a networkconnection from the refining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the refining environment is achemical refining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the refining environment is apharmaceutical refining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the refining environment is abiological refining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the refining environment is ahydrocarbon refining environment.

The present disclosure describes a system for self-organizing collectionand storage of data collection in a distribution environment, the systemaccording to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the distribution environment. Inembodiments, the plurality of sensor inputs is configured to sense atleast one of an operational mode, a fault mode and a health status of atleast one target system selected from a group consisting of a powersystem, a conveyor system, a robotic transport system, a robotichandling system, a packing system, a cold storage system, a hot storagesystem, a refrigeration system, a vacuum system, a hauling system, alifting system, an inspection system, and a suspension system, and aself-organizing system for self-organizing at least one of (i) a storageoperation of the data, (ii) a data collection operation of the sensorsthat provide the plurality of sensor inputs, and (iii) a selectionoperation of the plurality of sensor inputs.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemorganizes a swarm of mobile data collectors to collect data from aplurality of target systems.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemgenerates a storage specification for organizing the storage of thedata, the storage specification specifying data for local storage in thedistribution environment and specifying data for streaming via a networkconnection from the distribution environment.

Referencing FIG. 165, an example of the system 12200 for self-organized,network-sensitive data collection in an industrial environment isdepicted. The system 12200 includes an industrial system 12202 having anumber of components 12204, and a number of sensors 12206. Inembodiments, each of the sensors 12206 is operatively coupled to atleast one of the components 12204. The selection, distribution, type,and communicative setup of sensors depends upon the application of thesystem 12200 and/or the context.

In certain embodiments, sensor data values 12204 are provided to a datacollector 12208, which may be in communication with multiple sensors12206 and/or with a controller 12212. In certain embodiments, a plantcomputer 12210 is additionally or alternatively present. In the examplesystem, the controller 12212 is structured to functionally executeoperations of a sensor communication circuit 12224, sensor data storageprofile circuit 12226, sensor data storage implementation circuit 12228,storage planning circuit 12230, and/or haptic feedback circuit 12232.The controller 12212 is depicted as a separate device for clarity ofdescription. Aspects of the controller 12212 may be present on thesensors 12206, the data controller 12208, the plant computer 12210,and/or on a cloud computing device 12214. In certain embodimentsdescribed throughout this disclosure, all aspects of the controller12212 or other controllers may be present in another device depicted onthe system 12200. The plant computer 12210 represents local computingresources, for example processing, memory, and/or network resources,that may be present and/or in communication with the industrial system12202. In certain embodiments, the cloud computing device 12214represents computing resources externally available to the industrialsystem 12202, for example over a private network, intra-net, throughcellular communications, satellite communications, and/or over theinternet. In certain embodiments, the data controller 12208 may be acomputing device, a smart sensor, a MUX box, or other data collectiondevice capable to receive data from multiple sensors and to pass-throughthe data and/or store data for later transmission. An example of thedata controller 12208 has no storage and/or limited storage, andselectively passes sensor data therethrough, with a subset of the sensordata being communicated at a given time due to bandwidth considerationsof the data controller 12208, a related network, and/or imposed byenvironmental constraints. In certain embodiments, one or more sensorsand/or computing devices in the system 12200 are portable devices suchas the user associated device 12216 associated with a user 12218, forexample a plant operator walking through the industrial system may havea smart phone, which the system 12200 may selectively utilize as thedata controller 12208, the sensor 12206—for example to enhancecommunication throughput, sensor resolution, and/or as a primary methodfor communicating sensor data values 12244 to the controller 12212. Thesystem 12200 depicts the controller 12212, the sensors 12206, the datacontroller 12208, the plant computer 12210, and/or the cloud computingdevice 12214 having a memory storage for storing sensor data thereon,any one or more of which may not have a memory storage for storingsensor data thereon.

The example system 12200 further includes a mesh network 12220 having aplurality of network nodes depicted thereupon. The mesh network 12220 isdepicted in a single location for convenience of illustration, but itwill be understood that any network infrastructure that is within thesystem 12200, and/or within communication with the system 12200,including intermittently, is contemplated within the system network.Additionally, any or all of the cloud server 12214, the plant computer12210, the controller 12212, the data controller 12208, any networkcapable sensor 12206, and/or user the associated device 12216 may be apart of the network for the system, including the mesh network 12220,during at least certain operating conditions of the system 12200.Additionally, or alternatively, the system 12200 may utilize ahierarchical network, a peer-to-peer network, a peer-to-peer networkwith one or more super-nodes, combinations of these, hybrids of these,and/or may include multiple networks within the system 12200 or incommunication with the system. It will be appreciated that certainfeatures and operations of the present disclosure are beneficial to onlyone or more than one of these types of networks, certain features andoperations of the present disclosure are beneficial to any type ofnetwork, and certain features and operations are particularly beneficialto combinations of these networks, and/or to networks having multiplenetworking options within the network, where the benefits relate to theutilization of options of any type, or where the benefits relate to oneor more options being of a specific network type.

Referencing FIG. 166, an example apparatus 12222 includes the controller12212 having the sensor communication circuit 12224 that interprets anumber of sensor data values 12244 from the number of sensors 12206 anda system collaboration circuit 12228 that communicates at least aportion of the number of sensor data values (e.g., sensor data values12244 to target storage 12252) to a storage target computing deviceaccording to a sensor data transmission protocol 12232. The targetcomputing device includes any device in the system having memory that isthe target location for the selected sensor data 12252. For example, thecloud server 12214, the plant computer 12210, the user associated device12216, and/or another portion of the controller 12212 that communicateswith the sensor 12206 and/or the data controller 12208 over the networkof the system. The target computing device may be a short-term target(e.g., until a process operation is completed), a medium-term target(e.g., to be held until certain processing operations are completed onthe data, and/or until a periodic data migration occurs), and/or along-term target (e.g., to be held for the course of a data retentionpolicy, and/or until a long-term data migration is planned), and/or thedata storage target for an unknown period (e.g., data is passed to acloud server 12214, whereupon the system 12200, in certain embodiments,does not maintain control of the data). In certain embodiments, thetarget computing device is the next computing device in the systemplanned to store the data. In certain embodiments, the target computingdevice is the next computing device in the system where the data will bemoved, where such a move occurs across any aspect of the network of thesystem 12200.

The example controller 12212 includes a transmission environment circuit12226 that determines the transmission conditions 12254 corresponding tothe communication of the at least a portion of the number of sensor data12252 to the storage target computing device. The transmissionconditions 12254 include any conditions affecting the transmission ofthe data. For example, referencing FIG. 169, example and non-limitingtransmission conditions 12254 are depicted including the environmentalconditions 12272 (e.g., EM noise, vibration, temperature, the presenceand layout of devices or components affecting transmission, such asmetal, conductive, or high density) including the environmentalconditions 12272 that affect communications directly, and theenvironmental conditions 12272 that affect network devices such asrouters, servers, transmitters/transceivers, and the like. An example ofthe transmission conditions 12254 includes a network performance 12274,such as the specifications of network equipment or nodes, specifiedlimitations of network equipment or nodes (e.g., utilization limits,authorization for usage, available power, etc.), estimated limitationsof the network (e.g., based on equipment temperatures, noiseenvironment, etc.), and/or actual performance of the network (e.g., asobserved directly such as by timing messages, sending diagnosticmessages, or determining throughput, and/or indirectly by observingparameters such as memory buffers, arriving messages, etc. that tend toprovide information about the performance of the network). Anotherexample of the transmission condition 12254 includes network parameters12276, such as timing parameters 12278 (e.g., clock speeds, messagespeeds, synchronous speeds, asynchronous speeds, and the like), protocolselections 12280 (e.g., addressing information, message sizes includingadministrative support bits within messages, and/or speeds supported bythe protocols present or available), file type selections 12282 (e.g.,data transfer file types, stored file types, and the networkimplications such as how much data must be transferred before data is atleast partially readable, how to determine data is transferred, likelyor supported file sizes, and the like), streaming parameter selections12284 (e.g., streaming protocols, streaming speeds, priority informationof streaming data, available nodes and/or computing devices to managethe streaming data, and the like), and/or compression parameters 12286(e.g., compression algorithm and type, processing implications at eachend of the message, lossy versus lossless compression, how muchinformation must be passed prior to usable data being available, and thelike).

Referencing FIG. 170, certain further non-limiting examples of thetransmission conditions 12254 corresponding to the communication of thesensor data 12252 are depicted. Example and non-limiting transmissionconditions 12254 include a mesh network need 12288 (e.g., to rearrangethe mesh to balance throughput), a parent node connectivity change 12290in a hierarchically arranged network (e.g., the parent node has lostconnectivity, re-gained connectivity, and/or has changed to a differentset of child nodes and/or higher nodes), and/or a network super-node ina hybrid peer-to-peer application-layer network has been replaced 12292.A super-node, as utilized herein, is a node having additional capabilityfrom other peer-to-peer nodes. Such additional capability may be bydesign only—for example a super-node may connect in a different mannerand/or to nodes outside of the peer-to-peer node system. In certainembodiments, the super-node may additionally or alternatively have moreprocessing power, increased network speed or throughput access, and/ormore memory (e.g., for buffering, caching, and/or short term storage) toprovide more capability to meet the functions of the super-node.

An example of the transmission condition 12254 includes a node in a meshor hierarchical network detected as malicious (e.g., from anothersupervisory process, heuristically, or as indicated to the system12200); a peer node has experienced a bandwidth or connectivity change12296 (e.g., mesh network peer that was forwarding packets has lostconnectivity, gained additional bandwidth, had a reduction in availablebandwidth, and/or has regained connectivity). An example of thetransmission condition 12254 includes a change in a cost of transmittinginformation 12298 (e.g., cost has increased or decreased, where cost maybe a direct cost parameter such as a data transmission subscriptioncost, or an abstracted cost parameter reflecting overall systempriorities, and/or a current cost of delivering information over anetwork hop has changed), a change has been made in a hierarchicalnetwork arrangement (e.g., network arrangement change 12300) such as tobalance bandwidth use in a network tree; and/or a change in a permissionscheme 12302 (e.g., a portion of the network relaying sampling data hashad a change in permissions, authorization level, or credentials).Certain further examples of the transmission conditions 12254 includethe availability of an additional connection type 12304 (e.g., ahigher-bandwidth network connection type has become available, and/or alower-cost network connection type has become available); a change hasbeen made in a network topology 12306 (e.g., a node has gone offline oronline, a mesh change has occurred, and/or a hierarchy change hasoccurred); and/or a data collection client changed a preference or arequirement 12308 (e.g., a data frequency requirement for at least oneof the number of sensor values; a data type requirement for at least oneof the number of sensor values; a sensor target for data collection;and/or a data collection client has changed the storage target computingdevice, which may change the network delivery outcomes and routing).

The example controller 12212 includes a network management circuit 12230that updates the sensor data transmission protocol 12232 in response tothe transmission conditions 12254. For example, where the transmissionconditions 12254 indicate that a current routing, protocol, deliveryfrequency, delivery rate, and/or any other parameter associated withcommunicating the sensor data 12252 is no longer cost effective,possible, optimal, and/or where an improvement is available, the networkmanagement circuit 12230 updates the sensor data transmission protocol12232 in response to a lower cost, possible, optimal, and/or improvedtransmission condition. The example system collaboration circuit 12228is further responsive to the updated sensor data transmission protocol12232—for example, implementing subsequent communications of the sensordata 12252 in compliance with the updated sensor data transmissionprotocol 12232, providing a communication to the network managementcircuit 12230 indicating which aspects of the updated sensor datatransmission protocol 12232 cannot be or are not being followed, and/orproviding an alert (e.g., to an operator, a network node, controller12212, and/or the network management circuit 12230) indicating that achange is requested, indicating that a change is being implemented,and/or indicating that a requested change cannot be or is not beingimplemented.

An example of the system 12200 includes the transmission conditions12254 being environmental conditions 12272 relating to sensorcommunication of the number of sensor data values 12252, where thenetwork management circuit 12230 further analyzes the environmentalconditions 12272, and where updating the sensor data transmissionprotocol 12232 includes modifying the manner in which the number ofsensor data values are transmitted from the number of sensors 12206 tothe storage target computing device. An example system further includesthe data collector 12208 communicatively coupled to at least a portionof the number of sensors 12206 and responsive to the sensor datatransmission protocol 12232, where the system collaboration circuit12228 further receives the number of sensor data values 12244 from theat least a portion of the number of sensors, and where the transmissionconditions 12254 correspond to at least one network parametercorresponding to the communication of the number of sensor data valuesfrom the at least a portion of the number of sensors. Referencing FIG.171, a number of examples of the sensor data transmission protocol 12232values are depicted. An example of the sensor data transmission protocol12232 value includes a data collection rate 12310—for example a rateand/or a frequency at which the sensor 12206 transmits, provides, orsamples data, and/or at which the data collector 12208 receives, passesalong, stores, or otherwise captures sensor data. An example of thenetwork management circuit 12230 further updates the sensor datatransmission protocol 12232 to modify the data collector 12208 to adjustthe data collection rate 12310 for at least one of the number ofsensors. Another example of the sensor data transmission protocol 12232value includes a multiplexing schedule 12312, which includes the datacollector 12208 and/or a smart sensor configured to provide multiplesensor data values, such as in an alternating or other scheduled manner,and/or to package multiple sensor values into a single message in aconfigured manner. An example of the network management circuit 12230updates the sensor data transmission protocol 12232 to modify amultiplexing schedule of the data collector 12208 and/or smart sensor.Another example of the sensor data transmission protocol 12232 valueincludes an intermediate storage operation 12314, where an intermediatestorage is a storage at any location in the system at least one networktransmission prior to the target storage computing device. Intermediatestorage may be implemented as an on-demand operation, where a request ofthe data (e.g., from a user, a machine learning operation, or anothersystem component) results in the subsequent transfer from theintermediate storage to the target computing device, and/or theintermediate storage may be implemented to time shift networkcommunications to lower cost and/or lower network utilization times,and/or to manage moment-to-moment traffic on the network. The example ofthe network management circuit 12230 updates the sensor datatransmission protocol 12232 to command an intermediate storage operationfor at least a portion of the number of sensor data values, where theintermediate storage may be on a sensor, data collector, a node in themesh network, on the controller, on a component, and/or in any otherlocation within the system. An example of the sensor data transmissionprotocol 12232 includes a command for further data collection 12316 forat least a portion of the number of sensors—for example because aresolution, rate, and/or frequency of a sensor data provision is notsufficient for some aspect of the system, to provide additional data toa machine learning algorithm, and/or because a prior resource limitationis no longer applicable and further data from one or more sensors is nowavailable. An example of the sensor data transmission protocol 12232includes a command to implement a multiplexing schedule 12318—forexample where the data collector 12208 and/or smart sensor is capable tomultiplex sensor data but does not do so under all operating conditions,or only does so in response to the multiplexing schedule 12318 of thesensor data transmission protocol 12232.

An example of the network management circuit 12230 further updates thesensor data transmission protocol 12232 to adjust a network transmissionparameter (e.g., any network parameter 12276) for at least a portion ofthe number of sensor values. For example, certain network parametersthat are not control variables and/or are not currently being controlledare the transmission conditions 12254, and certain network parametersare control variables and subject to change in response to the sensordata transmission protocol 12232, and/or the network management circuit12230 can optionally take control of certain network parameters to makethem control variables. An example of the network management circuit12230 further updates the sensor data transmission protocol 12232 tochange any one or more of: a frequency of data transmitted; a quantityof data transmitted; a destination of data transmitted (including atarget or intermediate destination, and/or a routing); a networkprotocol used to transmit the data; and/or a network path (e.g.,providing a redundant path to transmit the data (e.g., where high noise,high network loss, and/or critical data are involved, the networkmanagement circuit 12230 may determine that the system operations areimproved with redundant pathing for some of the data)). An example ofthe network management circuit 12230 further updates the sensor datatransmission protocol 12232, such as to: bond an additional network pathto transmit the data (e.g., the network management circuit 12230 mayhave authority to bring additional network resources online, and/orselectively access additional network resources); re-arrange ahierarchical network to transmit the data (e.g., add or remove ahierarchy layer, change a parent-child relationship, etc., for example,to provide critical data with additional paths, fewer layers, and/or ahigher priority path); rebalance a hierarchical network to transmit thedata; and/or reconfigure a mesh network to transmit the data. An exampleof the network management circuit 12230 further updates the sensor datatransmission protocol 12232 to delay a data transmission time, and/ordelay the data transmission time to a lower cost transmission time.

An example network management circuit further updates the sensor datatransmission protocol 12232 to reduce the amount of information sent atone time over the network and/or updates the sensor data transmissionprotocol to adjust a frequency of data sent from a second data collector(e.g., an offset data collector within or not within the direct purviewof the network management circuit 12230, but where network resourceutilization from the second data collector competes with utilization ofthe first data collector).

An example of the network management circuit 12230 further adjusts anexternal data access frequency 12234—for example where an expert system12242 and/or a learning algorithm 12248 access the external data 12246to make continuous improvements to the system (e.g., accessinginformation outside of the sensor data values 12244, and/or from offsetsystems or aggregated cloud based data), and/or an external data accesstiming (12236). The control of the external data 12246 access allows forcontrol of network utilization when the system is low on resources, whenhigh fidelity and/or frequency of sensor data values 12244 isprioritized, and/or shifting of resource utilization into lower costportions of the operating space of the system. In certain embodiments,the system collaboration circuit 12228 accesses the external data 12246,and is responsive to the adjusted external data access frequency 12234and/or external data access timing value 12236. An example of thenetwork management circuit 12230 further adjusts a network utilizationvalue 12238—for example to keep system utilization operations below athreshold to reserve margin and/or to avoid the need for capital costupgrades to the system due to capacity limitations. An example of thenetwork management circuit 12230 adjusts the network utilization value12238 to utilize bandwidth at a lower cost bandwidth time—for examplewhen competing traffic is lower, when network utilization does notadversely affect other system processes, and/or when power consumptioncosts are lower.

An example of the network management circuit 12230 enables utilizing ahigh-speed network, and/or requests a higher cost bandwidth access, forexample when system process improvements are sufficient that highercosts are justified, to meet a minimum delivery requirement for data,and/or to move aging data from the system before it becomes obsolete ormust be deleted to make room for subsequent data.

An example of the network management circuit 12230 further includes theexpert system 12242, where the updating the sensor data transmissionprotocol 12232 is further in response to operations of the expert system12242. The self-organized, network-sensitive data collection system maymanage or optimize any such parameters or factors noted throughout thisdisclosure, individually or in combination, using an expert system,which may involve a rule-based optimization, optimization based on amodel of performance, and/or optimization using machinelearning/artificial intelligence, optionally including deep learningapproaches, or a hybrid or combination of the above. Referencing FIG.165, a number of non-limiting examples of the expert systems 12242, anyone or more of which may be present in embodiments having the expertsystem 12242. Without limitation to any other aspect of the presentdisclosure for expert systems, machine learning operations, and/oroptimization routines, example of the expert systems 12242 include arule-based system 12202 (e.g., seeded by rules based on modeling, expertinput, operator experience, or the like); a model-based system 12204(e.g., modeled responses or relationships in the system informingcertain operations of the expert system, and/or working with otheroperations of the expert system); a neural-net system (e.g., includingrules, state machines, decision trees, conditional determinations,and/or any other aspects); a Bayesian-based system 12208 (e.g.,statistical modeling, management of probabilistic responses orrelationships, and other determinations for managing uncertainty); afuzzy logic-based system 12210 (e.g., determining fuzzification statesfor various system parameters, state logic for responses, andde-fuzzification of truth values, and/or other determinations formanaging vague states of the system); and/or a machine learning system12212 (e.g., recursive, iterative, or other long-term optimization orimprovement of the expert system, including searching data, resolutions,sampling rates, etc. that are not within the scope of the expert systemto determine if improved parameters are available that are not presentlyutilized), which may be in addition to or an embodiment of the machinelearning algorithm 12248. Any aspect of the expert system 12242 may bere-calibrated, deleted, and/or added during operations of the expertsystem 12242, including in response to updated information learned bythe system, provided by a user or operator, provided by the machinelearning algorithm 12248, information from the external data 12246and/or from offset systems.

An example of the network management circuit 12230 further includes themachine learning algorithm 12248, where updating the sensor datatransmission protocol 12232 is further in response to operations of themachine learning algorithm 12248. An example of the machine learningalgorithm 12248 utilizes a machine learning optimization routine, andupon determining that an improved sensor data transmission protocol12232 is available, the network management circuit 12230 provides theupdated sensor data transmission protocol 12232 which is utilized by thesystem collaboration circuit 12228. In certain embodiments, the networkmanagement circuit 12230 may perform various operations such assupplying the sensor data transmission protocol 12232 which is utilizedby the system collaboration circuit 12228 to produce real-world results,applies modeling to the system (either first principles modeling basedon system characteristics, a model utilizing actual operating data forthe system, a model utilizing actual operating data for an offsetsystem, and/or combinations of these) to determine what an outcome ofthe given the sensor data transmission protocol 12232 will be or wouldhave been (including, for example, taking extra sensor data beyond whatis utilized to support a process operated by the system, and/orutilizing the external data 12246 and/or benchmarking data 12240),and/or applying randomized changes to the sensor data transmissionprotocol 12232 to ensure that an optimization routine does not settleinto a local optimum or non-optimal condition.

An example of the machine learning algorithm 12248 further utilizesfeedback data including the transmission conditions 12254, at least aportion of the number of sensor values 12244; and/or where the feedbackdata includes the benchmarking data 12240. Referencing FIG. 172,non-limiting examples of the benchmarking data 12240 are depicted. Thebenchmarking data 12240 may reference, generally, expected data (e.g.,according to the expert system 12242, user input, prior experience,and/or modeling outputs), data from an offset system (including asadjusted for differences in the contemplated system 12200), aggregateddata for similar systems (e.g., as the external data 12246 which may becloud-based), and the like. Benchmarking data may be relative to theentire system, the network, a node on the network, a data collector,and/or a single sensor or selected group of sensors. Example andnon-limiting benchmarking data includes a network efficiency 12320(e.g., throughput capability, power utilization, quality and/orintegrity of communications relative to the infrastructure, load cycle,and/or environmental conditions of the system 12200), a data efficiency12322 (e.g., a percentage of overall successful data captured relativeto a target value, a description of data gaps relative to a targetvalue, and/or may be focused on critical or prioritized data), acomparison with offset data collectors 12324 (e.g., comparing datacollectors in the system having a similar environment, data collectionresponsibility, or other characteristic making the comparisonmeaningful), a throughput efficiency 12326 (e.g., a utilization of theavailable throughput, a variability indicator—such as high variabilitybeing an indication that a network may be oversized or have furthertransmission capability, or high variability being an indication thatthe network is responsive to cost avoidance opportunities—or bothdepending upon the further context which can be understood looking atother information such as why the utilization differences occur), a dataefficacy 12328 (e.g., a determination that captured parameters areresult effective, strong control parameters, and/or highly predictiveparameters, and that efficacious data is taken at acceptable resolution,sampling rate, and the like), a data quality 12330 (e.g., degradation ofthe data due to noise, deconvolution errors, multiple calculationoperations and rounding, compression, packet losses, etc.), a dataprecision 12342 (e.g., a determination that sufficiently precise data istaken, preserved during communications, and preserved during storage), adata accuracy 12340 (e.g., a determination that corrupted data,degradation through transmission and/or storage, and/or time lag resultsin data that is alone inaccurate, or inaccurate as applied in a timesequence or other configuration), a data frequency 12338 (e.g., adetermination that data as communicated has sufficient time and/orfrequency domain resolution to determine the responses of interest), anenvironmental response 12336 (e.g., environmental effects on the networkare sufficiently managed to maintain other aspects of the data), asignal diversity 12332 (e.g., whether systematic gaps exist whichincrease the consequences of degradation—e.g., 1% of the data ismissing, but it's systematically a single critical sensor; do criticalsensed parameters have multiple potential sources of information), acritical response (is data sufficient to detect critical responses, suchas support for a sensor fusion operation and/or a pattern recognitionoperation), and/or a mesh networking coherence 12334 (e.g., keepingprocessors, nodes, and other network aspects together on a single viewof applicable memory states).

Referencing FIG. 173, certain further non-limiting examples of thebenchmarking data 12240 are depicted. Example and non-limiting exampleof the benchmarking data 12240 includes a data coverage 12346 (e.g.,what fraction of the desired data, critical data, etc. was successfullycommunicated and captured; how is the data distributed throughout thesystem), a target coverage 12344 (e.g., does a component or process ofthe system have sufficient time and spatial resolution of sensedvalues), a motion efficiency 12348 (e.g., reflecting an amount of time,number of steps, or extent of motion required to accomplish a givenresult, such as where an action is required by a human operator, roboticelement, drone, or the like to accomplish an action), a quality ofservice commitment 12358 (e.g., an agreement, formal or informalcommitment, and/or best practice quality of service such as maximum datagaps, minimum up-times, minimum percentages of coverage), a quality ofservice guarantee 12360 (e.g., a formal agreement to a quality ofservice with known or modeled consequences that can act in a costfunction, etc.), a service level agreement 12362 (e.g., minimum uptimes,data rates, data resolutions, etc., which may be driven by industrypractices, regulatory requirements, and/or formal agreements thatcertain parameters, detection for certain components, or detection forcertain processes in the system will meet data delivery requirements intype, resolution, sample rate, etc.), a predetermined quality of servicevalue (e.g., a user-defined value, a policy for the operator of thesystem, etc.), and/or a network obstruction value 12364. Example andnon-limiting network obstruction values 12364 include a networkinterference value (e.g., environmental noise, traffic on the network,collisions, etc.), a network obstruction value (e.g., a component,operation, and/or object obstructing wireless or wired communication ina region of the network, or over the entire network), and/or an area ofimpeded network connectivity (e.g., loss of connectivity for any reason,which may be normal at least intermittently during operations, or powerloss, movement of objects through the area, movement of a network nodethrough the area (e.g., a smart phone being utilized as a node), etc.).In certain embodiments, the network obstruction value 12364 may becaused by interference from a component of the system, an interferencecaused by one or more of the sensors (e.g., due to a fault or failure,or operation outside an expected range), interference caused by ametallic (or other conductive) object, interference caused by a physicalobstruction (e.g., a dense object blocking or reducing transparency towireless transmissions); an attenuated signal caused by a low powercondition (e.g., a brown-out, scheduled power reduction, low battery,etc.); and/or an attenuated signal caused by a network traffic demand ina portion of the network (e.g., a node or group of nodes has hightraffic demand during operations of the system).

Yet another example system includes an industrial system including anumber of components, and a number of sensors each operatively coupledto at least one of the number of components; a sensor communicationcircuit that interprets a number of sensor data values from the numberof sensors; a system collaboration circuit that communicates at least aportion of the number of sensor data values over a network having anumber of nodes to a storage target computing device according to asensor data transmission protocol; a transmission environment circuitthat determines transmission feedback corresponding to the communicationof the at least a portion of the number of sensor data values over thenetwork; and a network management circuit updates the sensor datatransmission protocol in response to the transmission feedback. Theexample system collaboration circuit is further responsive to theupdated sensor data transmission protocol.

Referencing FIG. 167, an example apparatus 12256 for self-organized,network-sensitive data collection in an industrial environment for anindustrial system having a network with a number of nodes is depicted.In addition to the aspects of the apparatus 12222, the apparatus 12256includes the system collaboration circuit 12228 further sending an alertto at least one of the number of nodes (e.g., as a node communication12258) in response to the updated sensor data transmission protocol12232. In certain embodiments, updating the sensor data transmissionprotocol 12232 includes the network management circuit 12230 includingnode control instructions, such as providing instructions to rearrange amesh network including the number of nodes, providing instructions torearrange a hierarchical data network including the number of nodes,rearranging a peer-to-peer data network including the number of nodes,rearranging a hybrid peer-to-peer data network including the number ofnodes. In certain embodiments, the system collaboration circuit 12228provides node control instructions as one or more node communications12258.

In certain embodiments, updating the sensor data transmission protocol12232 includes the network management circuit 12230 providinginstructions to reduce a quantity of data sent over the network;providing instructions to adjust a frequency of data capture sent overthe network; providing instructions to time-shift delivery of at least aportion of the number of sensor values sent over the network (e.g.,utilizing intermediate storage); providing instructions to change anetwork protocol corresponding to the network; providing instructions toreduce a throughput of at least one device coupled to the network;providing instructions to reduce a bandwidth use of the network;providing instructions to compress data corresponding to at least aportion of the number of sensor values sent over the network; providinginstructions to condense data corresponding to at least a portion of thenumber of sensor values sent over the network (e.g., providing arelevant subset, reduced sample rate data, etc.); providing instructionsto summarize data (e.g., providing a statistical description, anaggregated value, etc.) corresponding to at least a portion of thenumber of sensor values sent over the network; providing instructions toencrypt data corresponding to at least a portion of the number of sensorvalues sent over the network (e.g., to enable using an alternate, lesssecure network path, and/or to access another network path requiringencryption); providing instructions to deliver data corresponding to atleast a portion of the number of sensor values to a distributed ledger;providing instructions to deliver data corresponding to at least aportion of the number of sensor values to a central server (e.g., theplant computer 12210 and/or cloud server 12214); providing instructionsto deliver data corresponding to at least a portion of the number ofsensor values to a super-node; and providing instructions to deliverdata corresponding to at least a portion of the number of sensor valuesredundantly across a number of network connections. In certainembodiments, updating the sensor data transmission includes providinginstructions to deliver data corresponding to at least a portion of thenumber of sensor values to one of the components (e.g., where one ormore components 12204 in the system has memory storage and iscommunicatively accessible to the sensor 12206, the data collector12208, and/or the network), and/or where the one of the components iscommunicatively coupled to the sensor providing the data correspondingto at least a portion of the number of sensor values (e.g., where thedata to be stored on the component 12204 is the component the data wasmeasured for, or is in proximity to the sensor 12206 taking the data).

An example network includes a mesh network where the network managementcircuit 12230 further updates the sensor data transmission protocol12232 to provide instructions to eject (e.g., remove from the mesh map,take it out of service, etc.) one of the number of nodes from the meshnetwork. An example network includes a peer-to-peer network, where thenetwork management circuit 12230 further updates the sensor datatransmission protocol 12232 to provide instructions to eject one of thenumber of nodes from the peer-to-peer network.

An example of the network management circuit 12230 further updates thesensor data transmission protocol 12232 to cache (e.g., as a sensor datacache 12260) at least a portion of the number of sensor values 12252. Incertain further embodiments, the network management circuit 12230further updates the sensor data transmission protocol 12232 tocommunicate the cached sensor values 12260 in response to at least oneof: a determination that the cached data is requested (e.g., a user,model, machine learning algorithm, expert system, etc. has requested thedata); a determination that the network feedback indicates communicationof the cached data is available (e.g., a prior limitation on the networkleading the network management circuit 12230 to direct caching is nowlifted or improved); and/or a determination that higher priority data ispresent that requires utilization of cache resources holding the cacheddata 12260.

An example of the system 12200 for self-organized, network-sensitivedata collection in an industrial environment includes the industrialsystem 12202 having a number of components 12204 and a number of sensors12206 each operatively coupled to at least one of the number ofcomponents 12204. The sensor communication circuit 12224 interprets thenumber of sensor data values 12244 from the number of sensors at apredetermined frequency. The system collaboration circuit 12228 thatcommunicates at least a portion of the number of the sensor data values12252 over a network having a number of nodes to a storage targetcomputing device according to the sensor data transmission protocol12232, where the sensor data transmission protocol 12232 includes apredetermined hierarchy of data collection and the predeterminedfrequency. An example of the data management circuit 12230 adjusts thepredetermined frequency in response to the transmission conditions12254, and/or in response to the benchmarking data 12240.

An example of the system 12200 for self-organized, network-sensitivedata collection in an industrial environment includes the industrialsystem 12202 having a number of components 12204, and the number ofsensors 12206 each operatively coupled to at least one of the number ofcomponents 12204. The sensor communication circuit 12224 interprets anumber of sensor data values 12244 from the number of sensors 12206 at apredetermined frequency, and the system collaboration circuit 12228communicates at least a portion of the number of the sensor data values12252 over a network having a number of nodes to a storage targetcomputing device according to a sensor data transmission protocol. Atransmission environment circuit 12226 determines transmission feedback(e.g., the transmission conditions 12254) corresponding to thecommunication of the at least a portion of the number of the sensor datavalues 12252 over the network. The network management circuit 12230updates the sensor data transmission protocol 12232 in response to thetransmission feedback 12254, and a network notification circuit 12268provides an alert value 12264 in response to the updated sensor datatransmission protocol 12232. Example alert of the values 12264 include anotification to an operator, a notification to a user, a notification toa portable device associated with a user, a notification to a node ofthe network, a notification to a cloud computing device, a notificationto a plant computing device, and/or a provision of the alert as externaldata to an offset system. Example and non-limiting alert conditionsinclude a component of the system operating in a fault condition, aprocess of the system operating in a fault condition, a commencement ofthe utilization of cache storage and/or intermediate storage for sensorvalues due to a network communication limit, a change in the sensor datatransmission protocol (including changes of a selected type), and/or achange in the sensor data transmission protocol that may result in lossof data fidelity or resolution (e.g., compression of data, condensing ofdata, and/or summarizing data).

An example transmission feedback includes a feedback value such as: achange in transmission pricing, a change in storage pricing, a loss ofconnectivity, a reduction of bandwidth, a change in connectivity, achange in network availability, a change in network range, a change inlocal area network (LAN) connectivity, a change in wide area network(WAN) connectivity, and/or a change in wireless local area network(WLAN) connectivity.

An example system includes an assembly line industrial system having anumber of vibrating components, such as motors, conveyors, fans, and/orcompressors. The system includes a number of sensors that determinevarious parameters related to the vibrating components, includingdetermination of diagnostic and/or process related information (properoperation, off-nominal operation, operating speed, imminent servicing orfailure, etc.) of one or more of the components. Example sensors,without limitation, include noise, vibration, acceleration, temperature,and/or shaft speed sensors. The sensor information is conveyed to atarget storage system, including at least partially through a networkcommunicatively coupled to the assembly line industrial system. Theexample system includes a network management circuit that determines asensor data transmission protocol to control flow of data from thesensors to the target storage system. The network management circuit, arelated expert system, and/or a related machine learning algorithm,updates the sensor data transmission protocol to ensure efficientnetwork utilization, sufficient delivery of data to support systemcontrol, diagnostics, and/or other determinations planned for the dataoutside of the system, to reduce resource utilization of datatransmission, and/or to respond to system noise factors, variability,and/or changes in the system or related aspects such as cost orenvironment parameters. The example system includes improvement ofsystem operations to ensure that diagnostics, controls, or other datadependent operations can be completed, to reduce costs while maintainingperformance, and/or to increase system capability over time or processcycles.

An example system includes an automated robotic handling system,including a number of components such as actuators, gear boxes, and/orrail guides. The system includes a number of sensors that determinevarious parameters related to the components, including withoutlimitation actuator position and/or feedback sensors, vibration,acceleration, temperature, imaging sensors, and/or spatial positionsensors (e.g., within the handling system, a related plant, and/orGPS-type positioning). The sensor information is conveyed to a targetstorage system, including at least partially through a networkcommunicatively coupled to the automated robotic handling system. Theexample system includes a network management circuit that determines asensor data transmission protocol to control flow of data from thesensors to the target storage system. The network management circuit, arelated expert system, and/or a related machine learning algorithm,updates the sensor data transmission protocol to ensure efficientnetwork utilization, sufficient delivery of data to support systemcontrol, diagnostics, improvement and/or efficiency updates to handlingefficiency, and/or other determinations planned for the data outside ofthe system, to reduce resource utilization of data transmission, and/orto respond to system noise factors, variability, and/or changes in thesystem or related aspects such as cost or environment parameters. Theexample system includes improvement of system operations to ensure thatdiagnostics, controls, or other data dependent operations can becompleted, to reduce costs while maintaining performance, and/or toincrease system capability over time or process cycles.

An example system includes a mining operation, including a surfaceand/or underground mining operation. The example mining operationincludes components such as an underground inspection system, pumps,ventilation, generators and/or power generation, gas composition orquality systems, and/or process stream composition systems (e.g.,including determination of desired material compositions, and/orcomposition of effluent streams for pollution and/or regulatorycontrol). Various sensors are present in an example system to supportcontrol of the operation, determine status of the components, supportsafe operation, and/or to support regulatory compliance. The sensorinformation is conveyed to a target storage system, including at leastpartially through a network communicatively coupled to the miningoperation. In certain embodiments, the network infrastructure of themining operation exhibits high variability, due to, without limitation,significant environmental variability (e.g., pit or shaft conditionvariability) and/or intermittent availability—e.g., shutting offelectronics during certain mining operations, difficulty in providingnetwork access to portions of the mining operation, and/or thedesirability to include mobile or intermittently available deviceswithin the network infrastructure. The example system includes a networkmanagement circuit that determines a sensor data transmission protocolto control flow of data from the sensors to the target storage system.The network management circuit, a related expert system, and/or arelated machine learning algorithm, updates the sensor data transmissionprotocol to ensure efficient network utilization, sufficient delivery ofdata to support system control, diagnostics, improvement and/orefficiency updates to handling efficiency, support for financial and/orregulatory compliance, and/or other determinations planned for the dataoutside of the system, to reduce resource utilization of datatransmission, and/or to respond to system noise factors, variability,network infrastructure challenges, and/or changes in the system orrelated aspects such as cost or environment parameters.

An example system includes an aerospace system, such as a plane,helicopter, satellite, space vehicle or launcher, orbital platform,and/or missile. Aerospace systems have numerous systems supported bysensors, such as engine operations, control surface status andvibrations, environmental status (internal and external), and telemetrysupport. Additionally, aerospace systems have high variability in boththe number of sensors of varying types (e.g., a small number of fuelpressure sensors, but a large number of control surface sensors) as wellas the sampling rates for relevant determinations of sensors of varyingtypes (e.g., 1-second data may be sufficient for internal cabinpressure, but weather radar or engine speed sensors may require muchhigher time resolution). Computing power on an aerospace application isat a premium due to power consumption and weight considerations, andaccordingly iterative, recursive, deep learning, expert system, and/ormachine learning operations to improve any systems on the aerospacesystem, including sensor data taking and transmission of sensorinformation, are driven in many embodiments to computing devices outsideof the aerospace vehicle of the system (e.g., through offline learning,post-processing, or the like). Storage capacity on an aerospaceapplication is similarly at a premium, such that long-term storage ofsensor data on the aerospace vehicle is not a cost-effective solutionfor many embodiments. Additionally, network communication from anaerospace vehicle may be subject to high variability and/or bandwidthlimitations as the vehicle moves rapidly through the environment and/orinto areas where direct communication with ground-based resources is notpractical. Further, certain aerospace applications have significantcompetition for available network resources—for example in environmentswith a large number of passengers where passenger utilization of anetwork infrastructure consumes significant bandwidth. Accordingly, itcan be seen that operations of a network management circuit, a relatedexpert system, and/or a related machine learning algorithm, to updatethe sensor data transmission protocol can significantly enhance sensingoperations in various aerospace systems. Additionally, certain aerospaceapplications have a high number of offset systems, enhancing the abilityof an expert system or machine learning algorithm to improve sensor datacapture and transmission operations, and/or to manage the highvariability in sensed parameters (frequency, data rate, and/or dataresolution) for the system across operating conditions.

An example system includes an oil or gas production system, such as aproduction platform (onshore or offshore), pumps, rigs, drillingequipment, blenders, and the like. Oil and gas production systemsexhibit high variability in sensed variable types and sensingparameters, such as vibration (e.g., pumps, rotating shafts, fluid flowthrough pipes, etc.—which may be high frequency or low frequency), gascomposition (e.g., of a wellhead area, personnel zone, near storagetanks, etc.—where low frequency may typically be acceptable, and/or itmay be acceptable that no data is taken during certain times such aswhen personnel are not present), and/or pressure values (which may varysignificantly both in required resolution and frequency or sampling ratedepending upon operations currently occurring in the system).Additionally, oil and gas production systems have high variability innetwork infrastructure, both according to the system (e.g., an offshoreplatform versus a long-term ground-based production facility) andaccording to the operations being performed by the system (e.g., awellhead in production may have limited network access, while a drillingor fracturing operation may have significant network infrastructure at asite during operations). Accordingly, it can be seen that operations ofa network management circuit, a related expert system, and/or a relatedmachine learning algorithm, to update the sensor data transmissionprotocol can significantly enhance sensing operations in various oil orgas production systems.

The present disclosure describes system for self-organized,network-sensitive data collection in an industrial environment, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include an industrial system including a plurality ofcomponents, and a plurality of sensors each operatively coupled to atleast one of the plurality of components, a sensor communication circuitstructured to interpret a plurality of sensor data values from theplurality of sensors, a system collaboration circuit structured tocommunicate at least a portion of the plurality of sensor data values toa storage target computing device according to a sensor datatransmission protocol, a transmission environment circuit structured todetermine transmission conditions corresponding to the communication ofthe at least a portion of the plurality of sensor data values to thestorage target computing device, a network management circuit structuredto update the sensor data transmission protocol in response to thetransmission conditions. In embodiments, the system collaborationcircuit is further responsive to the updated sensor data transmissionprotocol.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transmission conditionsinclude environmental conditions relating to sensor communication of theplurality of sensor data values. In embodiments, the network managementcircuit is further structured to analyze the environmental conditions.In embodiments, updating the sensor data transmission protocol includesmodifying the manner in which the plurality of sensor data values istransmitted from the plurality of sensors to the storage targetcomputing device.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein a data collectorcommunicatively coupled to at least a portion of the plurality ofsensors and responsive to the sensor data transmission protocol. Inembodiments, the system collaboration circuit is structured to receivethe plurality of sensor data values from the at least a portion of theplurality of sensors. In embodiments, the transmission conditionscorrespond to at least one network parameter corresponding to thecommunication of the plurality of sensor data values from the at least aportion of the plurality of sensors.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol tomodify the data collector to adjust a data collection rate for at leastone of the plurality of sensors.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol tomodify a multiplexing schedule of the data collector.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol tocommand an intermediate storage operation for at least a portion of theplurality of sensor data values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol tocommand further data collection for at least a portion of the pluralityof sensors.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol tomodify the data collector to implement a multiplexing schedule.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol toadjust a network transmission parameter for at least a portion of theplurality of sensor values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusted networktransmission parameter includes at least one parameter selected from theparameters consisting of a timing parameter, a protocol selection, afile type selection, a streaming parameter selection, and a compressionparameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol tochange a frequency of data transmitted.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol tochange a quantity of data transmitted.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol tochange a destination of data transmitted.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol tochange a network protocol used to transmit the data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol toadd a redundant network path to transmit the data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol tobond an additional network path to transmit the data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol tore-arrange a hierarchical network to transmit the data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol torebalance a hierarchical network to transmit the data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol toreconfigure a mesh network to transmit the data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol todelay a data transmission time.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol todelay the data transmission time to a lower cost transmission time.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol toreduce the amount of information sent at one time over the network.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol toadjust a frequency of data sent from a second data collector.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to adjust an external data access frequency. Inembodiments, the system collaboration circuit is responsive to theadjusted external data access frequency.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to adjust an external data access timing value. Inembodiments, the system collaboration circuit is responsive to theadjusted external data access timing value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to adjust a network utilization value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to adjust the network utilization value to utilizebandwidth at a lower cost bandwidth time.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to enable utilizing a high-speed network.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to request a higher cost bandwidth access, and toupdate the sensor transmission protocol in response to the higher costbandwidth access.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitfurther includes an expert system. In embodiments, the updating thesensor data transmission protocol is further in response to operationsof the expert system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitfurther includes a machine learning algorithm. In embodiments, theupdating the sensor data transmission protocol is further in response tooperations of the machine learning algorithm.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the machine learning algorithmis further structured to utilize feedback data including thetransmission conditions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the feedback data furtherincludes at least a portion of the plurality of sensor values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the feedback data furtherincludes benchmarking data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the benchmarking data furtherincludes data selected from the list consisting of: a networkefficiency, a data efficiency, a comparison with offset data collectors,a throughput efficiency, a data efficacy, a data quality, a dataprecision, a data accuracy, and a data frequency.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the benchmarking data furtherincludes data selected from the list consisting of: an environmentalresponse, a mesh networking coherence, a data coverage, a targetcoverage, a signal diversity, a critical response, and a motionefficiency.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transmission conditionscorresponding to the communication comprise at least one conditionselected from the conditions consisting of a mesh network needs torearrange to balance throughput, a parent node in a hierarchicallyarranged network has had a change in connectivity, a network super-nodein a hybrid peer-to-peer application-layer network has been replaced,and a node in a mesh or hierarchical network has been detected asmalicious.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transmission conditionscorresponding to the communication comprise at least one conditionselected from the conditions consisting of a mesh network peerforwarding packets has lost connectivity, a mesh network peer forwardingpackets has gained additional bandwidth, a mesh network peer forwardingpackets has had a reduction in bandwidth, and a mesh network peerforwarding packets has regained connectivity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transmission conditionscorresponding to the communication comprise at least one conditionselected from the conditions consisting of a cost of transmittinginformation has changed dynamically, a change has been made in ahierarchical network arrangement to balance bandwidth use in a networktree, a portion of the network relaying sampling data has had a changein permissions, authorization level, or credentials, a current cost ofdelivering information over a network hop has changed, ahigher-bandwidth network connection type has become available, alower-cost network connection type has become available, and a changehas been made in a network topology.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transmission conditionscorresponding to the communication include at least one conditionselected from the conditions consisting of a data collection client haschanged a data frequency requirement for at least one of the pluralityof sensor values, a data collection client has changed a data typerequirement for at least one of the plurality of sensor values, a datacollection client has changed a sensor target for data collection, and adata collection client has changed the storage target computing device.

The present disclosure describes system for self-organized,network-sensitive data collection in an industrial environment, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include an industrial system including a plurality ofcomponents, and a plurality of sensors each operatively coupled to atleast one of the plurality of components, a sensor communication circuitstructured to interpret a plurality of sensor data values from theplurality of sensors, a system collaboration circuit structured tocommunicate at least a portion of the plurality of sensor data valuesover a network having a plurality of nodes to a storage target computingdevice according to a sensor data transmission protocol, a transmissionenvironment circuit structured to determine transmission feedbackcorresponding to the communication of the at least a portion of theplurality of sensor data values over the network, and a networkmanagement circuit structured to update the sensor data transmissionprotocol in response to the transmission feedback. In embodiments, thesystem collaboration circuit is further responsive to the updated sensordata transmission protocol.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the system collaborationcircuit is further structured to send an alert to at least one of theplurality of nodes in response to the updated sensor data transmissionprotocol.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein updating the sensor datatransmission includes at least one operation selected from theoperations consisting of providing instructions to rearrange a meshnetwork including the plurality of nodes, providing instructions torearrange a hierarchical data network including the plurality of nodes,rearranging a peer-to-peer data network including the plurality of nodesand rearranging a hybrid peer-to-peer data network including theplurality of nodes.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein updating the sensor datatransmission includes at least one operation selected from theoperations consisting of providing instructions to reduce a quantity ofdata sent over the network, providing instructions to adjust a frequencyof data capture sent over the network, providing instructions totime-shift delivery of at least a portion of the plurality of sensorvalues sent over the network, and providing instructions to change anetwork protocol corresponding to the network.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein updating the sensor datatransmission includes at least one operation selected from theoperations consisting of providing instructions to reduce a throughputof at least one device coupled to the network, providing instructions toreduce a bandwidth use of the network, providing instructions tocompress data corresponding to at least a portion of the plurality ofsensor values sent over the network, providing instructions to condensedata corresponding to at least a portion of the plurality of sensorvalues sent over the network, providing instructions to summarize datacorresponding to at least a portion of the plurality of sensor valuessent over the network, and providing instructions to encrypt datacorresponding to at least a portion of the plurality of sensor valuessent over the network.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein updating the sensor datatransmission includes at least one operation selected from theoperations consisting of providing instructions to deliver datacorresponding to at least a portion of the plurality of sensor values toa distributed ledger, providing instructions to deliver datacorresponding to at least a portion of the plurality of sensor values toa central server, providing instructions to deliver data correspondingto at least a portion of the plurality of sensor values to a super-nodeand providing instructions to deliver data corresponding to at least aportion of the plurality of sensor values redundantly across a pluralityof network connections.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein updating the sensor datatransmission includes providing instructions to deliver datacorresponding to at least a portion of the plurality of sensor values toone of the plurality of components.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the one of the plurality ofcomponents is communicatively coupled to the sensor providing the datacorresponding to at least a portion of the plurality of sensor values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the system collaborationcircuit is further structured to interpret a quality of servicecommitment. In embodiments, the network management circuit is furtherstructured to update the sensor data transmission protocol further inresponse to the quality of service commitment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the system collaborationcircuit is further structured to interpret a service level agreement. Inembodiments, the network management circuit is further structured toupdate the sensor data transmission protocol further in response to theservice level agreement.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol toprovide instructions to increase a quality of service value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network includes a meshnetwork. In embodiments, the network management circuit is furtherstructured to update the sensor data transmission protocol to provideinstructions to eject one of the plurality of nodes from the meshnetwork.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network includes apeer-to-peer network. In embodiments, the network management circuit isfurther structured to update the sensor data transmission protocol toprovide instructions to eject one of the plurality of nodes from thepeer-to-peer network.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol tocache at least a portion of the plurality of sensor values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitis further structured to update the sensor data transmission protocol tocommunicate the cached at least a portion of the plurality of sensorvalues in response to at least one of a determination that the cacheddata is requested, a determination that the network feedback indicatescommunication of the cached data is available, and a determination thathigher priority data is present that requires utilization of cacheresources holding the cached data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the system further includes adata collector configured to receive the at least a portion of theplurality of sensor data values. In embodiments, the at least a portionof the plurality of sensor data values includes data provided by aplurality of the sensors. In embodiments, the transmission feedbackincludes network performance information corresponding to the datacollector.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the system further includes adata collector configured to receive the at least a portion of theplurality of sensor data values. In embodiments, the at least a portionof the plurality of sensor data values includes data provided by aplurality of the sensors, a second data collector communicativelycoupled to the network. In embodiments, the transmission feedbackincludes network performance information corresponding to the seconddata collector.

The present disclosure describes system for self-organized,network-sensitive data collection in an industrial environment, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include an industrial system including a plurality ofcomponents, and a plurality of sensors each operatively coupled to atleast one of the plurality of components, a sensor communication circuitstructured to interpret a plurality of sensor data values from theplurality of sensors at a predetermined frequency, a systemcollaboration circuit structured to communicate at least a portion ofthe plurality of sensor data values over a network having a plurality ofnodes to a storage target computing device according to a sensor datatransmission protocol, the sensor data transmission protocol including apredetermined hierarchy of data collection and the predeterminedfrequency, a transmission environment circuit structured to determinetransmission feedback corresponding to the communication of the at leasta portion of the plurality of sensor data values over the network, and anetwork management circuit structured to update the sensor datatransmission protocol in response to the transmission feedback andfurther in response to benchmarking data. In embodiments, the systemcollaboration circuit is further responsive to the updated sensor datatransmission protocol.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein updating the sensor datatransmission includes at least one operation selected from theoperations consisting of providing an instruction to change the sensorsof the plurality of sensors, providing an instruction to adjust thepredetermined frequency, providing an instruction to adjust a quantityof the plurality of sensor data values that are stored, providing aninstruction to adjust a data transmission rate of the communication ofthe at least a portion of the plurality of sensor data values, providingan instruction to adjust a data transmission time of the communicationof the at least a portion of the plurality of sensor data values, andproviding an instruction to adjust a networking method of thecommunication over the network.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the benchmarking data furtherincludes data selected from the list consisting of a network efficiency,a data efficiency, a comparison with offset data collectors, athroughput efficiency, a data efficacy, a data quality, a dataprecision, a data accuracy, and a data frequency.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the benchmarking data furtherincludes data selected from the list consisting of an environmentalresponse, a mesh networking coherence, a data coverage, a targetcoverage, a signal diversity, a critical response, and a motion Afurther embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the benchmarking data furtherincludes data selected from the list consisting of a quality of servicecommitment, a quality of service guarantee, a service level agreement,and a predetermined quality of service value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the benchmarking data furtherincludes data selected from the list consisting of a networkinterference value, a network obstruction value, and an area of impedednetwork connectivity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transmission feedbackincludes a communication interference value selected from the valuesconsisting of an interference caused by a component of the system, aninterference caused by one of the sensors, an interference caused by ametallic object, an interference caused by a physical obstruction, anattenuated signal caused by a low power condition, and an attenuatedsignal caused by a network traffic demand in a portion of the network.

The present disclosure describes a system for self-organized,network-sensitive data collection in an industrial environment, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include an industrial system including a plurality ofcomponents, and a plurality of sensors each operatively coupled to atleast one of the plurality of components, a sensor communication circuitstructured to interpret a plurality of sensor data values from theplurality of sensors at a predetermined frequency, a systemcollaboration circuit structured to communicate at least a portion ofthe plurality of sensor data values over a network having a plurality ofnodes to a storage target computing device according to a sensor datatransmission protocol, a transmission environment circuit structured todetermine transmission feedback corresponding to the communication ofthe at least a portion of the plurality of sensor data values over thenetwork, a network management circuit structured to update the sensordata transmission protocol in response to the transmission feedback anda network notification circuit structured to provide an alert value inresponse to the updated sensor data transmission protocol. Inembodiments, the system collaboration circuit is further responsive tothe updated sensor data transmission protocol.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transmission feedbackincludes at least one feedback value selected from the values consistingof: a change in transmission pricing, a change in storage pricing, aloss of connectivity, a reduction of bandwidth, a change inconnectivity, a change in network availability, a change in networkrange, a change in WAN connectivity, and a change in WLAN connectivity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitfurther includes an expert system. In embodiments, the updating thesensor data transmission protocol is further in response to operationsof the expert system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system includes atleast one system selected from the systems consisting of: a rule-basedsystem, a model-based system, a neural-net system, a Bayesian-basedsystem, a fuzzy logic-based system, and a machine learning system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the network management circuitfurther includes a machine learning algorithm. In embodiments, theupdating the sensor data transmission protocol is further in response tooperations of the machine learning algorithm.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the machine learning algorithmis further structured to utilize feedback data including thetransmission conditions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the feedback data furtherincludes at least a portion of the plurality of sensor values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the feedback data furtherincludes benchmarking data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the benchmarking data furtherincludes data selected from the list consisting of: a networkefficiency, a data efficiency, a comparison with offset data collectors,a throughput efficiency, a data efficacy, a data quality, a dataprecision, a data accuracy, and a data frequency.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the benchmarking data furtherincludes data selected from the list consisting of: an environmentalresponse, a mesh networking coherence, a data coverage, a targetcoverage, a signal diversity, a critical response, and a motionefficiency.

Referencing FIG. 174, an example system 12500 for data collection in anindustrial environment includes an industrial system 12502 having anumber of components 12504, and a number of the sensors 12506. Inembodiments, each of the sensors 12506 is operatively coupled to atleast one of the components 12504. The selection, distribution, type,and communicative setup of sensors depends upon the application of thesystem 12500 and/or the context.

The example system 12500 further includes a sensor communication circuit12522 (reference FIG. 185) that interprets a number of the sensor datavalues 12542. An example system includes the sensor data values 12542being a number of values to support a sensor fusion operation, forexample a set of sensors believed to encompass detection of operatingconditions of the system that affect a desired output, to control aprocess or portion of the industrial system 12502, to diagnose orpredict an aspect of the industrial system 12502 or a process associatedwith the industrial system 12502.

In certain embodiments, the sensor data values 12542 are provided to adata collector 12508, which may be in communication with multiplesensors 12506 and/or with a controller 12512. In certain embodiments, aplant computer 12510 is additionally or alternatively present. In theexample system, the controller 12512 is structured to functionallyexecute operations of the sensor communication circuit 12522, the sensordata storage profile circuit 12524, the sensor data storageimplementation circuit 12526, a storage planning circuit 12528, and/or ahaptic feedback circuit 12530. The controller 12512 is depicted as aseparate device for clarity of description. Aspects of the controller12512 may be present on the sensors 12506, the data controller 12508,the plant computer 12510, and/or on a cloud computing device 12514. Incertain embodiments described throughout this disclosure, all aspects ofthe controller 12512 or other controllers may be present in anotherdevice depicted on the system 12500. The plant computer 12510 representslocal computing resources, for example processing, memory, and/ornetwork resources, that may be present and/or in communication with theindustrial system 12500. In certain embodiments, the cloud computingdevice 12514 represents computing resources externally available to theindustrial system 12502, for example over a private network, intra-net,through cellular communications, satellite communications, and/or overthe internet. In certain embodiments, the data controller 12508 may be acomputing device, a smart sensor, a MUX box, or other data collectiondevice capable to receive data from multiple sensors and to pass-throughthe data and/or store data for later transmission. An example of thedata controller 12508 has no storage and/or limited storage, andselectively passes sensor data therethrough, with a subset of the sensordata being communicated at a given time due to bandwidth considerationsof the data controller 12508, a related network, and/or imposed byenvironmental constraints. In certain embodiments, one or more sensorsand/or computing devices in the system 12500 are portable devices—forexample a plant operator walking through the industrial system may havea smart phone, which the system 12500 may selectively utilize as thedata controller 12508, the sensor 12506—for example to enhancecommunication throughput, sensor resolution, and/or as a primary methodfor communicating the sensor data values 12542 to the controller 12512.The system 12500 depicts the controller 12512, the sensors 12506, thedata controller 12508, the plant computer 12510, and/or the cloudcomputing device 12514 having a memory storage for storing sensor datathereon, any one or more of which may not have a memory storage forstoring sensor data thereon. In certain embodiments, the sensor datastorage profile circuit 12524 prepares a data storage profile 12532 thatdirects sensor data to memory storage, including moving sensor data in acontrolled manner from one memory storage to another. Sensor data storedon various devices consumes memory on the device, transferring thestored data between device consumes network and/or communicationbandwidth in the system 12500, and/or operations on sensor data such asprocessing, compression, statistical analysis, summarization, and/orprovision of alerts consumes processor cycles as well as memory tosupport operations such as buffer files, intermediate data, and thelike. Accordingly, improved or optimal configuration and/or updating ofthe data storage profile 12532 provides for lower utilization of systemresources and/or allows for the storage of sensor data with higherresolution, over longer time frames, and/or from a larger number ofsensors.

Referencing FIG. 175, an example apparatus 12520 for self-organizingdata storage for a data collector for an industrial system is depicted.The example apparatus 12520 includes a controller, such as thecontroller 12512. The example controller includes the sensorcommunication circuit 12522 that interprets a number of the sensor datavalues 12542, and the sensor data storage profile circuit 12524 thatdetermines the data storage profile 12532. The data storage profile12532 includes a data storage plan for the number of the sensor datavalues 12542. The data storage plan includes how much of the sensor datavalues 12542 is stored initially (e.g., as the data is sampled, and/orafter initial transmission to the data controller 12508, the plantcomputer 12510, the controller 12512, and/or the cloud-computing device12514). The example data storage profile 12532 includes a plan for thetransmission of data, which may be according to a time, a process stage,operating conditions of the system 12500 and/or a network related to thesystem, as well as the communication conditions of devices within thesystem 12500.

For example, data from a temperature sensor may be planned to be storedlocally on a sensor having storage capacity, and transmitted in burststo a data controller. The data controller may be instructed to transmitthe sensor data to the cloud computing device on a schedule, for exampleas the data controller memory reaches a threshold, as networkcommunication capacity is available, at the conclusion of a process,and/or upon request. Additionally or alternatively, data from thesensors may be changed on a device or upon transfer of the data (e.g.,just before transfer, just after transfer, or on a schedule). Forexample, the data storage profile 12532 may describe storing highresolution, high precision, and/or high-sampling rate data, and reducingthe storage of the data set after a period of time, a selected event,and/or confirmation of a successful process or that the high resolutiondata is no longer needed. Accordingly, higher resolution data and/ordata from a large number of sensors may be available for utilization,such as by a sensor fusion operation or the like, while the long-termmemory utilization is also managed. Each of the sensor data sets may betreated individually for memory storage characteristics, and/or sensorsmay be grouped for similar treatment (e.g., sensors having similar datacharacteristics and/or impact on the system, sensors cooperating in asensor fusion operation, a group of sensors utilized for a model or avirtual sensor, etc.). In certain embodiments, sensor data from a singlesensor may be treated distinctly according to an update of the datastorage profile 12532, a time or process stage at which the data istaken, and/or a system condition such as a network issue, a faultcondition, or the like. Additionally or alternatively, a single set ofsensor data may be stored in multiple places in the system, for examplewhere the same data is utilized in several separate sensor fusionoperations, and the resource consumption from storing multiple sets ofthe same data is lower than a processor or network utilization toutilize a single stored data set in several separate processes.

Referencing FIG. 179, various aspects of an example of the data storageprofile 12532 are depicted. The example data storage profile 12532includes aspects of the data storage profile 12532 that may be includedas additional or alternative aspects of the data storage profile 12532relative to a storage location definition 12534, the storage timedefinition, and/or a storage time definition 12536, a data resolutiondescription 12540, and/or may be included as aspects of these. Any oneor more of the factors or parameters relating to storage depicted inFIG. 179 may be included in the data storage profile 12532 and/ormanaged by a self-organizing storage system (e.g., system 12500 and/orcontroller 12532). The self-organizing storage system may manage oroptimize any such parameters or factors noted throughout thisdisclosure, individually or in combination, using an expert system,which may involve a rule-based optimization, optimization based on amodel of performance, and/or optimization using machinelearning/artificial intelligence, optionally including deep learningapproaches, or a hybrid or combination of the above. In embodiments, anexample of the data storage profile 12532 includes a storage type plan12576 or profile that accounts for or specifies a type of storage, suchas based on the underlying physical media type of the storage, the typeof device or system on which storage resides, the mechanism by whichstorage can be accessed for reading or writing data, or the like. Forexample, a storage media plan 12578 may specify or account for use oftape media, hard disk drive media, flash memory media, non-volatilememory, optical media, one-time programmable memory, or the like. Thestorage media plan may account for or specify parameters relating to themedia, including capabilities such as storage duration, power usage,reliability, redundancy, thermal performance factors, robustness toenvironmental conditions (such as radiation or extreme temperatures),input/output speeds and capabilities, writing speeds, reading speeds,and the like, or other media specific parameters such as data fileorganization, operating system, read-write life cycle, data error rates,and/or data compression aspects related to or inherent to the media ormedia controller. A storage access plan 12580 or profile may specify oraccount for the nature of the interface to available storage, such asdatabase storage (including relational, object-oriented, and otherdatabases, as well as distributed databases, virtual machines,cloud-based databases, and the like), cloud storage (such as S3™ bucketsand other simple storage formats), stream-based storage, cache storage,edge storage (e.g., in edge-based network nodes), on-device storage,server-based storage, network-attached storage or the like. The storageaccess plan or profile may specify or account for factors such as thecost of different storage types, input/output performance, reliability,complexity, size, and other factors. A storage protocol plan 12582 orprofile may specify or account for a protocol by which data will betransmitted or written, such as a streaming protocol, an IP-basedprotocol, a non-volatile memory express protocol, a SATA protocol orother network-attached storage protocol, a disk-attached storageprotocol, an Ethernet protocol, a peered storage protocol, a distributedledger protocol, a packet-based storage protocol, a batch-based storageprotocol, a metadata storage protocol, a compressed storage protocol(using various compression types, such as for packet-based media,streaming media, lossy or lossless compression types, and the like), orothers. The storage protocol plan may account for or specify factorsrelating to the storage protocol, such as input/output performance,compatibility with available network resources, cost, complexity, dataprocessing required to implement the protocol, network utilization tosupport the protocol, robustness of the protocol to support system noise(e.g., EM, competing network traffic, interruption frequency of networkavailability), memory utilization to implement the protocol (such as:as-stored memory utilization, and/or intermediate memory utilization increating or transferring the data), and the like. A storage writingprotocol 12584 plan or profile may specify or account for how data willbe written to storage, such as in file form, in streaming form, in batchform, in discrete chunks, to partitions, in stripes or bands acrossdifferent storage locations, in streams, in packets or the like. Thestorage writing protocol may account for or specify parameters andfactors relating to writing, such as input speed, reliability,redundancy, security, and the like. A storage security plan 12586 orprofile may account for or specify how storage will be secured, such asavailability or type of password protection, authentication,permissioning, rights management, encryption (of the data, of thestorage media, and/or of network traffic on the system), physicalisolation, network isolation, geographic placement, and the like. Astorage location plan 12588 or profile may account for or specify alocation for storage, such as a geolocation, a network location (e.g.,at the edge, on a given server, or within a given cloud platform orplatforms), or a location on a device, such as a location on a datacollector, a location on a handheld device (such as a smart phone,tablet, or personal computer of an operator within an environment), alocation within or across a group of devices (such as a mesh, apeer-to-peer group, a ring, a hub-and-spoke group, a set of paralleldevices, a swarm of devices (such as a swarm of collectors), or thelike), a location in an industrial environment (such as or within astorage element of an instrumentation system of or for a machine, alocation on an information technology system for the environment, or thelike), or a dedicated storage system, such as a disk, dongle, USBdevice, or the like. A storage backup plan 12590 or profile may accountfor or specify a plan for backup or redundancy of stored data, such asindicating redundant locations and managing any or all of the abovefactors for a backup storage location. In certain embodiments, thestorage security plan 12586 and/or the storage backup plan 12590 mayspecify parameters such as data retention, long-term storage plans(e.g., migrate the stored data to a different storage media after aperiod of time and/or after certain operations in the system areperformed on the data), physical risk management of the data and/orstorage media (e.g., provision of the data in multiple geographicregions having distinct physical risk parameters, movement of the datawhen a storage location experiences a physical risk, refreshing the dataaccording to a predicted life cycle of a long-term storage media, etc.).

The example controller 12512 further includes the sensor data storageimplementation circuit 12526 that stores at least a portion of thenumber of sensor data values in response to the data storage profile12532. An example of the controller 12512 includes the data storageprofile 12532 having the storage location definition 12534 correspondingto at least one of the number of the sensor data values 12542, includingat least one location such as: a sensor storage location (e.g., datastored for a period of time on the sensor, and/or on a portable devicefor a user 12518 in proximity to the industrial system 12502 where theportable device is adapted by the system as a sensor), a sensorcommunication device storage location (e.g., the data controller 12508,MUX device, smart sensor in communication with other sensors, and/or ona portable device for the user 12518 in proximity to the industrialsystem 12502 or a network of the industrial system 12502 where theportable device is adapted by the system as a communication device totransfer sensor data between components in the system, etc.), a regionalnetwork storage location (e.g., on the plant computer 12510 and/or thecontroller 12512), and/or a global network storage location (e.g., onthe cloud computing device 12514).

An example of the controller 12512 includes the data storage profile12532 including the storage time definition 12536 corresponding to atleast one of the number of the sensor data values 12542, including atleast one time value such as: a time domain description over which thecorresponding at least one of the number of sensor data values is to bestored (e.g., times and locations for the data, which may includerelative time to some aspect such as the time of data sampling, aprocess stage start or stop time, etc., or an absolute time such asmidnight, Saturday, the first of the month, etc.); a time domain storagetrajectory including a number of time values corresponding to a numberof storage locations over which the corresponding at least one of thenumber of sensor data values is to be stored (e.g., the flow of thesensor data through the system across a number of devices, with the timefor each storage transfer including a relative or absolute timedescription); a process description value over which the correspondingat least one of the number of sensor data values is to be stored (e.g.,including a process description and the planned storage location fordata values during the described process portion; the processdescription can include stages of a process, and identification of whichprocess is related to the storage plan, and the like); and/or a processdescription trajectory including a number of process stagescorresponding to a number of storage locations over which thecorresponding at least one of the number of sensor data values is to bestored (e.g., the flow of the sensor data through the system across anumber of devices, with process stage and/or process identification foreach storage transfer).

An example of the controller 12512 includes the data storage profile12532 including the data resolution description 12540 corresponding toat least one of the number of sensor data values 12544, where the dataresolution description 12540 includes a value such as: a detectiondensity value corresponding to the at least one of the number of sensordata values (e.g., detection density may be time sampling resolution,spatial sampling resolution, precision of the sampled data, and/or aprocessing operation to be applied that may affect the availableresolution, such as filtering and/or lossy compression of the data); adetection density value corresponding to a more than one of the numberof the sensor data values (e.g., a group of sensors having similardetection density values, a secondary data value determined from a groupof sensors having a specified detection density value, etc.); adetection density trajectory including a number of detection densityvalues of the at least one of the number of sensor data values, each ofthe number of detection density values corresponding to a time value(e.g., any of the detection density concepts combined with any of thetime domain concepts); a detection density trajectory including a numberof detection density values of the at least one of the number of sensordata values, each of the number of detection density valuescorresponding to a process stage value (e.g., any of the detectiondensity concepts combined with any of the process description or stageconcepts); and/or a detection density trajectory comprising a number ofdetection density values of the at least one of the number of sensordata values, each of the number of detection density valuescorresponding to a storage location value (e.g., detection density canbe varied according to the device storing the data).

An example of the sensor data storage profile circuit 12524 furtherupdates the data storage profile 12532 after the operations of thesensor data storage implementation circuit 12526, where the sensor datastorage implementation circuit 12526 further stores the portion of thenumber of sensor data values 12544 in response to the updated datastorage profile 12532. For example, during operations of a system at afirst point in time, the sensor data storage implementation circuit12526 utilizes a currently existing data storage profile of the sensordata storage implementation circuit 12526, which may be based on initialestimates of the system performance, desired data from an operator ofthe system, and/or from a previous operation of the sensor data storageprofile circuit 12524. During operations of the system, the sensor datastorage implementation circuit 12526 stores data according to the datastorage profile 12532, and the sensor data storage profile circuit 12524determines parameters for the data storage profile 12532 which mayresult in improved performance of the system. An example of the sensordata storage profile circuit 12524 tests various parameters for the datastorage profile 12532, for example utilizing a machine learningoptimization routine, and upon determining that an improved data storageprofile 12532 is available, the sensor data storage profile circuit12524 provides the updated data storage profile 12532 which is utilizedby the sensor data storage implementation circuit 12526. In certainembodiments, the sensor data storage profile circuit 12524 may performvarious operations such as supplying an intermediate data storageprofile 12532 which is utilized by the sensor data storageimplementation circuit 12526 to produce real-world results, appliesmodeling to the system (either first principles modeling based on systemcharacteristics, a model utilizing actual operating data for the system,a model utilizing actual operating data for an offset system, and/orcombinations of these) to determine what an outcome of a given datastorage profile 12532 will be or would have been (including, forexample, taking extra sensor data beyond what is utilized to support aprocess operated by the system), and/or applying randomized changes tothe data storage profile 12532 to ensure that an optimization routinedoes not settle into a local optimum or non-optimal condition.

An example of the sensor data storage profile circuit 12524 furtherupdates the data storage profile 12532 in response to external data12544 and/or cloud-based data 12538, including data such as: an enhanceddata request value (e.g., an operator, model, optimization routine,and/or other process requests enhanced data resolution for one or moreparameters); a process success value (e.g., indicating that currentstorage practice provides for sufficient data availability and/or systemperformance; and/or that current storage practice may be over-capable,and one or more changes to reduce system utilization may be available);a process failure value (e.g., indicating that current storage practicesmay not provide for sufficient data availability and/or systemperformance, which may include additional operations or alerts to anoperator to determine whether the data transmission and/or availabilitycontributed to the process failure); a component service value (e.g., anoperation to adjust the data storage to ensure higher resolution data isavailable to improve a learning algorithm predicting future serviceevents, and/or to determine which factors may have contributed topremature service); a component maintenance value (e.g., an operation toadjust the data storage to ensure higher resolution data is available toimprove a learning algorithm predicting future maintenance events,and/or to determine which factors may have contributed to prematuremaintenance); a network description value (e.g., a change in thenetwork, for example by identification of devices, determination ofprotocols, and/or as entered by a user or operator, where the networkchange results in a capability change and potentially a distinct optimalstorage plan for sensor data); a process feedback value (e.g., one ormore process conditions detected); a network feedback value (e.g., oneor more network changes as determined by actual operations of thenetwork—e.g., a loss or reduction in communication of one or moredevices, a network communication volume change, a transmission noisevalue change on the network, etc.); a sensor feedback value (e.g.,metadata such as a sensor fault, capability change; and/or based on thedetected data from the system, for example an anomalous reading, rate ofchange, or off-nominal condition indicating that enhanced or reducedresolution, sampling time, etc. should change the storage plan); and/ora second data storage profile, where the second data storage profile wasgenerated for an offset system.

An example of the storage planning circuit 12528 determines a dataconfiguration plan 12546 and updates the data storage profile 12532 inresponse to the data configuration plan 12546, where the sensor datastorage implementation circuit 12526 further stores at least a portionof the number of sensor data values in response to the updated datastorage profile 12532. An example of the data configuration plan 12546includes a value such as: a data storage structure value (e.g., a datatype, such as integer, string, a comma delimited file, how many bits arecommitted to the values, etc.); a data compression value (e.g., whetherto compress data, a compression model to use, and/or whether segments ofdata can be replaced with summary information, polynomial or other curvefit summarizations, etc.); a data write strategy value (e.g., whether tostore values in a distributed manner or on a single device, whichnetwork communication and/or operating system protocols to utilize); adata hierarchy value (e.g., which data is favored over other data wherestorage constraints and/or communication constraints will limit thestored data—the limits may be temporal, such as data will not be in theintended location at the intended time, or permanent, such as some datawill need to be compressed in a lossy manner, and/or lost); an enhancedaccess value determined for the data (e.g., the data is of a type forreports, searching, modeling access, and/or otherwise tagged, whereenhanced access includes where the data is stored for scope ofavailability, indexing of data, summarization of data, topical reportsof data, which may be stored in addition to the raw or processed sensordata); and/or an instruction value corresponding to the data (e.g., aplaceholder indicating where data can be located, an interface to accessthe data, metadata indicating units, precision, time frames, processesin operation, faults present, outcomes, etc.).

It can be seen that the provision of control over data flow and storagethrough the system allows for improvement generally, and movement towardoptimization over time, of data management throughout the system.Accordingly, more data of a higher resolution can be accumulated, and ina more readily accessible manner, than previously known systems withfixed or manually configurable data storage and flow for a givenutilization of resources such as storage space, communication bandwidth,power consumption, and/or processor execution cycles. Additionally, thesystem can respond to process variations that affect the optimal orbeneficial parameters for controlling data flow and storage. One ofskill in the art, having the benefit of the disclosures herein, willrecognize that combinations of control of data storage schemes with datatype control and knowledge about process operations for a system createpowerful combinations in certain contemplated embodiments. For example,data of a higher resolution can be maintained for a longer period andmade available if a need for the data arises, without incurring the fullcost of storing the data permanently and/or communicating the datathroughout every layer of the system.

In an embodiment, in an underground mining inspection system, certaindetailed data regarding toxic gas concentrations, temperatures, noise,etc. may need to be captured and stored for regulatory purposes, but forongoing operational purposes, perhaps only a single data point regardingone or more toxic gases is needed periodically. In this embodiment, thedata storage profile for the system may indicate that only certainsensor data aligned with regulatory needs be stored in a certain mannerthat is long term and optionally only available as needed, while othersensor data required operationally be stored in a more accessiblemanner.

In another embodiment involving automotive brakes for fleet vehicles,data regarding brake use and performance may be acquired at highresolution and stored in a first data storage that is not transmittedthroughout the network, while lower resolution data are transmittedperiodically and/or in near real time to a fleet control and maintenanceapplication. Should the application or other user require higherresolution data, it may be accessed from the first data storage.

In a further embodiment of manufacturing body and frame components oftrucks and cars, certain detailed data regarding paint color, surfacecurvature, and other quality control measures may be captured and storedat high resolution, but for ongoing operational purposes, only lowresolution data regarding throughput are transmitted. In thisembodiment, the data storage profile for the system may indicate thatonly certain sensor data aligned with quality control needs be stored ina certain manner that is long term and optionally only available asneeded, while other sensor data required operationally be stored in amore accessible manner.

In another example, data types, resolution, and the like can beconfigured and changed as the data flows through the system, accordingto values that are beneficial for the individual components handling thedata, according to the utilized networking resources for the data,and/or according to accompanying data (e.g., a model, virtual sensor,and/or sensor fusion operation) where higher capability data would notimprove the precision of the process utilizing the accompanying data.

In an embodiment, in rail condition monitoring systems, as railcondition data are acquired, each component of the system may requiredifferent resolutions of the same data. Continuing with this example, asreal-time rail traffic data are acquired, these data may be storedand/or transmitted at low resolution in order to quickly disseminate thedata throughout the system, while utilization and load data may bestored and utilized at higher resolution to track rail use fees and needfor rail maintenance at a more granular level.

In another embodiment of a hydraulic pump operating in a tractor, as thetractor is in the field and does not have access to a network, data fromon-board sensors may be acquired and stored in a local manner on thetractor at low resolution, but when the tractor regains access, data maybe acquired and transmitted at high resolution.

In yet another embodiment of an actuator in a robotic handling unit inan automotive plant, data regarding the actuator may flow into multipledownstream systems, such as a production tracking system that utilizesthe actuator data alone and an energy efficiency tracking system thatutilizes the data in a sensor fusion with data from environmentalsensors. Resolution of the actuator data may be configured differentlyas it is transmitted to each of these systems for their disparate uses.

In still another embodiment of a generator in a mine, data may beacquired regarding the performance of the generator, carbon monoxidelevels near the generator and a cost for running the generator. Eachcomponent of a control system overseeing the mine may require differentresolutions of the same data. Continuing with this example, as carbonmonoxide data are acquired, these data may be stored and/or transmittedat low resolution in order to quickly disseminate the data throughoutthe system in order to properly alert workers. Performance and cost datamay be stored and utilized at higher resolution to track economicefficiency and lifetime maintenance needs.

In an additional embodiment, sensors on a truck's wheel end may monitorlubrication, noise (e.g., grinding, vibration) and temperature. While inthe field, sensor data may be transmitted remotely at low resolution forremote monitoring, but when within a threshold distance from a fleetmaintenance facility, data may be transmitted at high resolution.

In another example, accompanying information for the data allows forefficient downstream processing (e.g., by a downstream device or processaccessing the data) including unpackaging the data, readily determiningwhere related higher capability data may be present in the system,and/or streamlining operations utilizing the data (e.g., reporting,modeling, alerting, and/or performing a sensor fusion or other systemanalysis). An embodiment includes storing high capability (e.g.,high-sampling rate, high precision, indexed, etc.) in a first storagedevice in the system (e.g., close to the sensors in the network layer topreserve network communication resources) and sending lower capabilitydata up the network layers (e.g., to a cloud-computing device), wherethe lower capability data includes accompanying information to accessthe stored high capability data, including accompanying data that may beaccessible to a user (e.g., a header, message box, or other organicallyinterfaceable accompanying data) and/or accessible to an automatedprocess (e.g., structured data, WL, populated fields, or the like) wherethe process can utilize the accompanying data to automatically request,retrieve, or access the high capability data. In certain embodiments,accompanying data may further include information about the content,precision, sampling time, calibrations (e.g., de-bouncing, filtering, orother processing applied) such that an accessing component or user candetermine without retrieving the high capability data whether such datawill meet the desired parameters.

In an embodiment, vibration noise from vibration sensors attached tovibrators on an assembly line may be stored locally in a high resolutionformat while a low resolution version of the same data with accompanyinginformation regarding the availability of ambient and local noise datafor a sensor fusion may be transmitted to a cloud-based server. If aresident process on the server requires the high resolution data, suchas a machine learning process, the server may retrieve the data at thattime.

In another embodiment of an airplane engine, performance data aggregatedfrom a plurality of sensors may be transmitted while in flight alongwith accompanying information to a remote site. The accompanyinginformation, such as a header with metadata relating to historical planeinformation, may allow the remote site to efficiently analyze theperformance data in the context of the historical data without having toaccess additional databases.

In a further embodiment of a coal crusher in a power generationfacility, data accompanying low quality sensor data regarding the sizeof coal exiting the crusher may include information about the precisionin the size measurement such that a technician can determine if thehigher resolution data are needed to confirm a determination that thecrusher needs to come offline for maintenance.

In yet a further embodiment of a drilling machine or production platformemployed in oil and gas production, high capability data may be acquiredand stored locally regarding parameters of the drill's and platform'soperation, but only low capability data are transmitted off-site toconserve bandwidth. Along with the low capability data, accompanyinginformation may include instructions on how an automated off-siteprocess can automatically access the high capability data in the eventthat it is required.

In still a further embodiment, temperature sensors on a pump employed inoil & gas production or mining may be stored locally in a highresolution format while a low resolution version of the same data withaccompanying information regarding the availability of noise and energyuse data for a sensor fusion may be transmitted to a cloud-based server.If a resident process on the server requires the high resolution data,such as a machine learning process, the server may retrieve the data atthat time.

In another embodiment of a gearbox in an automatic robotic handling unitor an agricultural setting, performance data aggregated from a pluralityof sensors may be transmitted while in use along with accompanyinginformation to a remote site. The accompanying information, such as aheader with metadata relating to historical gearbox information, mayallow the remote site to efficiently analyze the performance data in thecontext of the historical data without having to access additionaldatabases.

In a further embodiment of a ventilation system in a mine, dataaccompanying low quality sensor data regarding the size of particulatesin the air may include information about the precision in the sizemeasurement such that a technician can determine if the higherresolution data are needed to confirm a determination that theventilation system requires maintenance.

In yet a further embodiment of a rolling bearing employed inagriculture, high capability data may be acquired and stored locallyregarding parameters of the rolling bearing's operation, but only lowcapability data are transmitted off-site to conserve bandwidth. Alongwith the low capability data, accompanying information may includeinstructions on how an automated off-site process can automaticallyaccess the high capability data in the event that it is required.

In a further embodiment of a stamp mill in a mine, data accompanying lowquality sensor data regarding the size of mineral deposits exiting thestamp mill may include information about the precision in the sizemeasurement such that a technician can determine if the higherresolution data are needed to confirm a determination that the stampmill requires a change in an operation parameter.

Referencing FIG. 176, an example of the storage time definition 12536 isdepicted. The example of the storage time definition 12536 depicts anumber of storage locations 12556 corresponding to a number of timevalues 12558. It is understood that any values such as storage types,storage media, storage access, storage protocols, storage writingvalues, storage security, and/or storage backup values, may be includedin the storage time definition 12536. Additionally or alternatively, anexample of the storage time definition 12536 may include processoperations, events, and/or other values in addition to or as analternative to time values 12558. The example of the storage timedefinition 12536 depicts movement of related sensor data to a firststorage location 12550 over a first time interval, to a second storagelocation 12552 over a second time internal, and to a third storagelocation 12554 over a third time interval. The storage location values12550, 12552, 12554 are depicted as an integral selection correspondingto planned storage locations, but additionally or alternatively thevalues may be continuous or discrete, but not necessarily integralvalues. For example, a storage location value 12550 of “1” may beassociated with a first storage location, and a storage location value12550 of “2” may be associated with a second storage location, where avalue between “1” and “2” has an understood meaning—such as aprioritization to move the data (e.g., a “1.1” indicates that the datashould be moved from “2” to “1” with a relatively high priority comparedto a “1.4”), a percentage of the data to be moved (e.g., to controlnetwork utilization, memory utilization, or the like during a transferoperation), and/or a preference for a storage location with alternativeoptions (e.g., to allow for directing storage location, and inclusion ina cost function such that storage location can be balanced with otherconstraints in the system). Additionally or alternatively, the storagetime definition 12536 can include additional dimensions (e.g., changingprotocols, media, security plans, etc.) and/or can include multipleoptions for the storage plan (e.g., providing a weighted value between2, 3, 4, or more storage locations, protocols, media, etc. in atriangulated or multiple-dimension definition space).

Referencing FIG. 177 an example of the data resolution description 12540is depicted. The example of the data resolution description 12540depicts a number of data resolution values 12562 corresponding to anumber of time values 12564. It is understood that any values such asstorage types, storage media, storage access, storage protocols, storagewriting values, storage security, and/or storage backup values, may beincluded in the data resolution description 12540. Additionally oralternatively, an example of the data resolution description 12540 mayinclude process operations, events, and/or other values in addition toor as an alternative to time values 12558. The example of the dataresolution description 12540 depicts changes in the resolution of storedrelated sensor data resolution values 12560 over time intervals, forexample operating at a low resolution initially, stepping up to a higherresolution (e.g., corresponding to a process start time), to a highresolution value (e.g., during a process time where the process issignificantly improved by high resolution of the related sensor data),and to a low resolution value (e.g., after a completion of the process).The example depicts a higher resolution before the process starts thanafter the process ends as an illustrative example, but the dataresolution description 12540 may include any data resolution trajectory.The data resolution values 12560 are depicted as integral selectionscorresponding to planned data resolutions, but additionally oralternatively the values may be continuous or discrete, but notnecessarily integral values. For example, data resolution values 12560of “1” may be associated with a first data resolution (e.g., a specificsampling time, byte resolution, etc.), and a data resolution values12560 of “2” may be associated with a second data resolution, where avalue between “1” and “2” has an understood meaning—such as aprioritization to sample at the defined resolution (e.g., a “1.1”indicates the data should be taken at a sampling rate corresponding to“1” with a relatively high priority compared to a “1.3”, and/or at asampling rate 10% of the way between the rate between “1” and “2”),and/or a preference for a data resolution with alternative options(e.g., to allow for sensor or network limitations, available sensorcommunication devices such as a data controller, smart sensor, orportable device taking the data from the sensor, and/or inclusion in acost function such that data resolution can be balanced with otherconstraints in the system). Additionally or alternatively, the dataresolution description 12540 can include additional dimensions (e.g.,changing protocols, media, security plans, etc.) and/or can includemultiple options for the data resolution plan (e.g., providing aweighted value between 2, 3, 4, or more data resolution values,protocols, media, etc. in a triangulated or multiple-dimensiondefinition space).

An example system 12500 further includes the haptic feedback circuit12530 that determines a haptic feedback instruction 12548 in response toat least one of the number of sensor values 12542 and/or the datastorage profile 12532, and a haptic feedback device 12516 responsive tothe haptic feedback instruction 12548. Example and non-limiting thehaptic feedback instructions 12548 include an instruction such as: avibration command; a temperature command; a sound command; an electricalcommand; and/or a light command. Example and non-limiting operations ofthe haptic feedback circuit 12530 include feedback that data is storedor being stored on the haptic feedback device 12516 and/or on a portabledevice associated with the user 12518 in communication with the hapticfeedback device 12516 (e.g., the user 12518 traverses through the system12500 with a smart phone, which the system 12500 utilizes to storesensor data, and provides the haptic feedback instructions 12548 tonotify the user 12518 that the smart phone is currently being utilizedby the system 12500, for example allowing the user 12518 to remain incommunication with the sensor, data controller, or other transmittingdevice, and/or allowing the user to actively cancel or enable the datatransfer). Additionally or alternatively, the haptic feedback device12516 may be the smart phone (e.g., utilizing vibration, sound, light,or other haptic aspects of the smart phone), and/or the haptic feedbackdevice 12516 may include data storage and/or communication capabilities.

In certain embodiments, the haptic feedback circuit 12530 provides thehaptic feedback instruction 12548 as an alert or notification to theuser 12518, for example to alert or notify the user 12518 that a processhas commenced or is about to start, that an off-nominal operation isdetected or predicted, that a component of the system requires or ispredicted to require maintenance, that an aspect of the system is in acondition that the user 12518 may want to be aware of (e.g., a componentis still powered, has high potential energy of any type, is at a highpressure, and/or is at a high temperature where the user 12518 may be inproximity to the component), that a data storage related aspect of thesystem is in a noteworthy condition (e.g., a data storage component ofthe system is at capacity, out of communication, is in a faultcondition, has lost contact with a sensor, etc.), to request a responsefrom the user 12518 (e.g., an approval to start a process, datatransfer, process rate change, clear a fault, etc.) In certainembodiments, the haptic feedback circuit 12530 configures the hapticfeedback instruction 12548 to provide an intuitive feedback to the user12518. For example, an alert value may provide a more rapid, urgent,and/or intermittent vibration mode relative to an informationalnotification; a temperature based alert or notification may utilize atemperature based haptic feedback (e.g., an overtemperature vesselnotification may provide a warm or cold haptic feedback) and/or flashinga color that is associated with the temperature (e.g., flashing red foran overtemperature or blue for an under-temperature); an electricallybased notification may provide an electrically associated hapticfeedback (e.g., a sound associated with electricity such as a buzzing orsparking sound, or even a mild electrical feedback such as when a useris opening a panel for a component that is still powered); providing avibration feedback for a bearing, motor, or other rotating or vibratingcomponent that is operating off-nominally; and/or providing a requestedfeedback to the user based upon sensed data (e.g., transmitting avibration profile to the haptic feedback device that is analogous to thedetected vibration in a requested component for example allowing anexpert user to diagnose the component without physical contact;providing a haptic feedback for a requested component for example if theuser is double checking a lockout/tagout operation before entering acomponent, opening a panel, and/or entering a potentially hazardousarea). The provided examples for operations of the haptic feedbackcircuit 12530 are non-limiting illustrations.

Referencing FIG. 178, an example apparatus for data collection in anindustrial environment 12566 includes the controller 12512 the sensorcommunication circuit 12522 that interprets a number of the sensor datavalues 12542, the sensor data storage profile circuit 12524 thatdetermines the data storage profile 12532, where the data storageprofile 12532 includes the data storage plan for the number of thesensor data values 12542, and the network coding circuit 12568 thatprovides a network coding value 12570 in response to the number of thesensor data values 12542 and the data storage profile 12532. Thecontroller 12512 further includes the sensor data storage implementationcircuit 12526 that stores at least a portion of the number of the sensordata values 12542 in response to the data storage profile 12532 and thenetwork coding value 12570. The network coding value 12570 includes,without limitation, network encoding for data transmission, such aspacket sizing, distribution, combinations of sensor data within packets,encoding and decoding algorithms for network data and communications,and/or any other aspects of controlling network communicationsthroughout the system. In certain embodiments, the network coding value12570 includes a linear network coding algorithm, a random linearnetwork coding algorithm, and/or a convolutional code. Additionally oralternatively, the network coding circuit 12568 provides schedulingand/or synchronization for network communication devices of the system,and can include separate scheduling and/or synchronization for separatenetworks in the system. The network coding circuit 12568 schedules thenetwork coding value 12570 throughout the system according to the datavolumes, transfer rates, and network utilization, and alternatively oradditionally performs a self-learning and/or machine learning operationto improve or optimize network coding. For example, a sensor having asingle low-volume data transfer to a data controller may utilize TCP/IPpacket communication to the data controller without linear networkcoding, while higher volume aggregated data transfer from the datacontroller to another system component (e.g., the controller 12512) mayutilize linear network coding. The example of the network coding circuit12568 adjusts the network coding value 12570 in real time for thecomponents in the system to optimize or improve transfer rates, powerutilization, errors and lost packets, and/or any other desiredparameters. For example, a given component may have resulting lowtransfer rates but a large available memory, while a downstreamcomponent has a lower available memory (potentially relative to the datastorage expectation for that component), and accordingly a complexnetwork coding value 12570 for the given component may not result inimproved throughput of data throughout the system, while the networkcoding value 12570 enhancing throughput for the downstream component mayjustify the processing overhead for a more complex network coding value12570.

An example system includes the network coding circuit 12568 furtherdetermining a network definition value 12572, and providing the networkcoding value 12570 further in response to the network definition value12572. Example of the network definition values 12572 include valuessuch as: a network feedback value (e.g., transfer rates, up time,synchronization availability, etc.); a network condition value (e.g.,presence of noise, transmission/receiver capability, drop-outs, etc.); anetwork topology value (e.g., the communication flow and connectivity ofdevices; operating systems, protocols, and storage types of devices;available computing resources on devices; the location and function ofdevices in the system); an intermittently available network device value(e.g., a known or observed availability for the device over time orprocess stage; predicted availability of the device; prediction of knownnoise factors for the device, such as process operations that reducedevice availability); and/or a network cost description value (e.g.,resource utilization of the device, including relative cost or impact ofprocessing, memory, and/or communication resources; power utilizationand cost of power consumption for devices; available power for thedevice and a cost description for externalities related to consuming thepower—such as for a battery where the power itself may not be expensivebut the power in the specific location has a cost associated withreplacement, including availability or access to the device duringoperations).

An example system includes the network coding circuit 12568 furtherproviding the network coding value 12570 such that the sensor datastorage implementation circuit stores a first portion of the number ofthe sensor data values 12542 utilizing a first network coding value12570, and a second portion of the number of the sensor data values12542 utilizing a second network coding value 12570 (e.g., the networkcoding values 12570 can vary with the data being transmitted, thetransmitting device, and/or over time or process stage). Example andnon-limiting network coding values include: a network type selection(e.g., public, private, wireless, wired, intranet, external, internet,cellular, etc.), a network selection (e.g., which one or more of anavailable number of networks will be utilized), a network codingselection (e.g., packet definitions, encoding techniques, linear,randomized linear, convolution, triangulated, etc.), a network timingselection (e.g., synchronization and sequencing of data transmissionsbetween devices), a network feature selection (e.g., turning on or offnetwork support devices or repeaters; enabling, disabling, or adjustingsecurity selections; increasing or decreasing a power of a device,etc.), a network protocol selection (e.g., TCP/IP, FTP, Wi-Fi,Bluetooth, Ethernet, and/or routing protocols); a packet size selection(including header and/or parity information); and/or a packet orderingselection (e.g., determining how to transmit the various sensorinformation that may be on a device, and/or determining the packet todata value correspondence). An example network coding circuit 12568further adjusts the network coding value 12570 to provide anintermediate network coding value (e.g., as a test coding value on thesystem, and/or as a modeled coding value being run off-line), to comparea performance indicator 12574 corresponding to each of the networkcoding value 12570 and the intermediate network coding value, and toprovide an updated network coding value (e.g., as the network codingvalue 12570) in response to the comparison of the performance indicators12574.

An example system includes an industrial system having a number ofcomponents, and a number of sensors each operatively coupled to at leastone of the number of components. The number of sensors provide a numberof sensor values, and the system further includes a number of organizingstructures such as a controller, a data collector, a plant computer, acloud-based server and/or global computing device, and/or a networklayer, where the organizing structures are configured forself-organizing storage of at least a portion of the number of sensorvalues. For example, operations of the controller 12512 provide forstorage and distribution of sensor data values to reduce consumption ofresources (processor, network, and/or memory) for storing sensor data.The self-organizing operations include management of the stored sensordata over time, including providing sensor information to systemcomponents in time to complete operations therefore (e.g., control,improvement, modeling, and/or machine learning for process operations ofthe system). Additionally, data security, including long-term securitydue to storage media, geographic, and/or unauthorized access, isconsidered throughout the data storage life cycle. An example systemfurther includes the organizing structures providing enhanced resolutionof the number of sensor values in response to at least one of anenhanced data request value or an alert value corresponding to theindustrial system. The system provides enhanced resolution bycontrolling the storage processes to address system impact, includingkeeping lower resolution, summary, or other accessibility dataavailable, and storing higher resolution data in a lower resourceutilization manner which is available upon request and/or at a timeappropriate to system operations. Example enhanced resolution includes:an enhanced spatial resolution, an enhanced time domain resolution, agreater number of the number of sensor values than a standard resolutionof the number of sensor values, and/or a greater precision of at leastone of the number of sensor values than a standard resolution of thenumber of sensor values. An example system further includes a networklayer, where the organizing structures are configured forself-organizing network coding for communication of the number of sensorvalues on the network layer. An example system further includes a hapticfeedback device of a user in proximity to at least one of the industrialsystem or the network layer, and where the organizing structures areconfigured for providing haptic feedback to the haptic feedback device,and/or for configuring the haptic feedback to provide an intuitive alertto the user.

In embodiments, a system for data collection in an industrialenvironment may comprise: a sensor communication circuit structured tointerpret a plurality of sensor data values; a sensor data storageprofile circuit structured to determine a data storage profile, the datastorage profile comprising a data storage plan for the plurality ofsensor data values; and a sensor data storage implementation circuitstructured to store at least a portion of the plurality of sensor datavalues in response to the data storage profile. In embodiments, the datastorage profile may include a storage location definition correspondingto at least one of the plurality of sensor data values, the storagelocation definition comprising at least one location selected from thelocations consisting of: a sensor storage location, a sensorcommunication device storage location, a regional network storagelocation, and a global network storage location. The data storageprofile may include a storage time definition corresponding to at leastone of the plurality of sensor data values, the storage time definitioncomprising at least one time value selected from the time valuesconsisting of: a time domain description over which the corresponding atleast one of the plurality of sensor data values is to be stored; a timedomain storage trajectory comprising a plurality of time valuescorresponding to a plurality of storage locations over which thecorresponding at least one of the plurality of sensor data values is tobe stored; a process description value over which the corresponding atleast one of the plurality of sensor data values is to be stored; and aprocess description trajectory comprising a plurality of process stagescorresponding to a plurality of storage locations over which thecorresponding at least one of the plurality of sensor data values is tobe stored. The data storage profile may include a data resolutiondescription corresponding to at least one of the plurality of sensordata values. In embodiments, the data resolution description comprisesat least one of: a detection density value corresponding to the at leastone of the plurality of sensor data values; a detection density valuecorresponding to a plurality of the at least one of the plurality of thesensor data values; a detection density trajectory comprising aplurality of detection density values of the at least one of theplurality of sensor data values, each of the plurality of detectiondensity values corresponding to a time value; a detection densitytrajectory comprising a plurality of detection density values of the atleast one of the plurality of sensor data values, each of the pluralityof detection density values corresponding to a process stage value; anda detection density trajectory comprising a plurality of detectiondensity values of the at least one of the plurality of sensor datavalues, each of the plurality of detection density values correspondingto a storage location value. The sensor data storage profile circuit maybe further structured to update the data storage profile after theoperations of the sensor data storage implementation circuit. Inembodiments, the sensor data storage implementation circuit is furtherstructured to store the portion of the plurality of sensor data valuesin response to the updated data storage profile. The sensor data storageprofile circuit may be further structured to update the data storageprofile in response to external data, the external data comprising atleast one data value selected from the data values consisting of: anenhanced data request value; a process success value; a process failurevalue; a component service value; a component maintenance value; anetwork description value; a process feedback value; a network feedbackvalue; a sensor feedback value; and a second data storage profile, thesecond data storage profile generated for an offset system. A storageplanning circuit may be structured to determine a data configurationplan, to update the data storage profile in response to the dataconfiguration plan. In embodiments, the sensor data storageimplementation circuit is further structured to store the at least aportion of the plurality of sensor data values in response to theupdated data storage profile. The data configuration plan may include atleast one value selected from the values consisting of: a data storagestructure value; a data compression value; a data write strategy value;a data hierarchy value; an enhanced access value determined for thedata; and an instruction value corresponding to the data. A hapticfeedback circuit may be structured to determine a haptic feedbackinstruction in response to at least one of the plurality of sensorvalues or the data storage profile; and a haptic feedback deviceresponsive to the haptic feedback instruction. The haptic feedbackinstruction may include at least one instruction selected from theinstructions consisting of: a vibration command; a temperature command;a sound command; an electrical command; and a light command. The datastorage plan may be generated by a rule-based expert system utilizingfeedback. In embodiments, the feedback relates to one or more of anaspect of the industrial environment or the plurality of sensor datavalues. The data storage plan may be generated by a model-based expertsystem utilizing feedback. In embodiments, the feedback relates to oneor more of an aspect of the industrial environment or the plurality ofsensor data values. The data storage plan may be generated by aniterative expert system utilizing feedback. In embodiments, the feedbackrelates to one or more of an aspect of the industrial environment or theplurality of sensor data values. The data storage plan may be generatedby a deep learning machine system utilizing feedback. In embodiments,the feedback relates to one or more of an aspect of the industrialenvironment or the plurality of sensor data values. The data storageplan may be based on one or more an underlying physical media type ofthe storage, a type of device or system on which storage resides, and amechanism by which storage can be accessed for reading or writing data.The underlying physical media may be one of a tape media, a hard diskdrive media, a flash memory media, a non-volatile memory, an opticalmedia, and a one-time programmable memory. The data storage plan mayaccount for or specify a parameter relating to the underlying physicalmedia comprising one or more of a storage duration, a power usage, areliability, a redundancy, a thermal performance factor, a robustness toenvironmental conditions, an input/output speed and capability, awriting speed, a reading speed, a data file organization, an operatingsystem, a read-write life cycle, a data error rate, and a datacompression aspect related to or inherent to the underlying physicalmedia or a media controller. The data storage plan may include one ormore of a storage type plan, a storage media plan, a storage accessplan, a storage protocol plan, a storage writing protocol plan, astorage security plan, a storage location plan, and a storage backupplan.

In embodiments, a system for data collection in an industrialenvironment may comprise: a sensor communication circuit structured tointerpret a plurality of sensor data values; a sensor data storageprofile circuit structured to determine a data storage profile, the datastorage profile comprising a data storage plan for the plurality ofsensor data values; a network coding circuit structured to provide anetwork coding value in response to the plurality of sensor data valuesand the data storage profile; and a sensor data storage implementationcircuit structured to store at least a portion of the plurality ofsensor data values in response to the data storage profile and thenetwork coding value. The network coding circuit may be structured todetermine a network definition value, and to provide the network codingvalue further in response to the network definition value. Inembodiments, the network definition value comprises at least one valueselected from the values consisting of: a network feedback value; anetwork condition value; a network topology value; an intermittentlyavailable network device value; and a network cost description value.The network coding circuit may be structured to provide the networkcoding value such that the sensor data storage implementation circuitstores a first portion of the plurality of sensor data values utilizinga first network coding value, and a second portion of the plurality ofsensor data values utilizing a second network coding value. The networkcoding value may include at least one of the values selected from thevalues consisting of: a network type selection, a network selection, anetwork coding selection, a network timing selection, a network featureselection, a network protocol selection, a packet size selection, and apacket ordering selection. The network coding circuit may be furtherstructured to adjust the network coding value to provide an intermediatenetwork coding value, to compare a performance indicator correspondingto each of the network coding value and the intermediate network codingvalue, and to provide an updated network coding value in response to thecomparison of the performance indicators.

In embodiments, a system may comprise: an industrial system comprising aplurality of components, and a plurality of sensors each operativelycoupled to at least one of the plurality of components; the plurality ofsensors providing a plurality of sensor values; and a means forself-organizing storage of at least a portion of the plurality of sensorvalues. In embodiments, a means may be provided for enhancing resolutionof the plurality of sensor values in response to at least one of anenhanced data request value or an alert value corresponding to theindustrial system. In embodiments, the enhanced resolution comprises atleast one of an enhanced spatial resolution, an enhanced time domainresolution, a greater number of the plurality of sensor values than astandard resolution of the plurality of sensor values, and a greaterprecision of at least one of the plurality of sensor values than thestandard resolution of the plurality of sensor values. The system mayinclude a network layer, and a means for self-organizing network codingfor communication of the plurality of sensor values on the networklayer. The system may include a means for providing haptic feedback to ahaptic feedback device of a user in proximity to at least one of theindustrial system or the network layer. The system may include a meansfor configuring the haptic feedback to provide an intuitive alert to theuser.

In embodiments, a system for self-organizing data storage for datacollected from a mine may comprise: a sensor communication circuitstructured to interpret a plurality of sensor data values; a sensor datastorage profile circuit structured to determine a data storage profile,the data storage profile comprising a data storage plan for theplurality of sensor data values; and a sensor data storageimplementation circuit structured to store at least a portion of theplurality of sensor data values in response to the data storage profile.In embodiments, the system may include a self-organizing data storagefor data collected from an assembly line, including: a sensorcommunication circuit structured to interpret a plurality of sensor datavalues; a sensor data storage profile circuit structured to determine adata storage profile, the data storage profile comprising a data storageplan for the plurality of sensor data values; and a sensor data storageimplementation circuit structured to store at least a portion of theplurality of sensor data values in response to the data storage profile.

In embodiments, a system for self-organizing data storage for datacollected from an agricultural system may comprise: a sensorcommunication circuit structured to interpret a plurality of sensor datavalues; a sensor data storage profile circuit structured to determine adata storage profile, the data storage profile comprising a data storageplan for the plurality of sensor data values; and a sensor data storageimplementation circuit structured to store at least a portion of theplurality of sensor data values in response to the data storage profile.

In embodiments, a system for self-organizing data storage for datacollected from an automotive robotic handling unit may comprise: asensor communication circuit structured to interpret a plurality ofsensor data values; a sensor data storage profile circuit structured todetermine a data storage profile, the data storage profile comprising adata storage plan for the plurality of sensor data values; and a sensordata storage implementation circuit structured to store at least aportion of the plurality of sensor data values in response to the datastorage profile.

In embodiments, a system for self-organizing data storage for datacollected from an automotive system may comprise: a sensor communicationcircuit structured to interpret a plurality of sensor data values; asensor data storage profile circuit structured to determine a datastorage profile, the data storage profile comprising a data storage planfor the plurality of sensor data values; and a sensor data storageimplementation circuit structured to store at least a portion of theplurality of sensor data values in response to the data storage profile.

In embodiments, a system for self-organizing data storage for datacollected from an automotive robotic handling unit may include: a sensorcommunication circuit structured to interpret a plurality of sensor datavalues; a sensor data storage profile circuit structured to determine adata storage profile, the data storage profile comprising a data storageplan for the plurality of sensor data values; and a sensor data storageimplementation circuit structured to store at least a portion of theplurality of sensor data values in response to the data storage profile.

In embodiments, a system for self-organizing data storage for datacollected from an aerospace system may comprise: a sensor communicationcircuit structured to interpret a plurality of sensor data values; asensor data storage profile circuit structured to determine a datastorage profile, the data storage profile comprising a data storage planfor the plurality of sensor data values; and a sensor data storageimplementation circuit structured to store at least a portion of theplurality of sensor data values in response to the data storage profile.

In embodiments, a system for self-organizing data storage for datacollected from a railway may include: a sensor communication circuitstructured to interpret a plurality of sensor data values; a sensor datastorage profile circuit structured to determine a data storage profile,the data storage profile comprising a data storage plan for theplurality of sensor data values; and a sensor data storageimplementation circuit structured to store at least a portion of theplurality of sensor data values in response to the data storage profile.

In embodiments, a system for self-organizing data storage for datacollected from an oil and gas production system may comprise: a sensorcommunication circuit structured to interpret a plurality of sensor datavalues; a sensor data storage profile circuit structured to determine adata storage profile, the data storage profile comprising a data storageplan for the plurality of sensor data values; and a sensor data storageimplementation circuit structured to store at least a portion of theplurality of sensor data values in response to the data storage profile.

In embodiments, a system for self-organizing data storage for datacollected from a power generation system, the system comprising: asensor communication circuit structured to interpret a plurality ofsensor data values; a sensor data storage profile circuit structured todetermine a data storage profile, the data storage profile comprising adata storage plan for the plurality of sensor data values; and a sensordata storage implementation circuit structured to store at least aportion of the plurality of sensor data values in response to the datastorage profile.

In embodiments, methods and systems are provided for data collection inor relating to one or more machines deployed in an industrialenvironment using self-organized network coding for network transmissionof sensor data in a network. In embodiments, network coding may be usedto specify and manage the manner in which packets (including streams ofpackets as noted in various embodiments disclosed throughout thisdisclosure and the documents incorporated by reference) are relayed froma sender (e.g., a data collector, instrumentation system, computer, orthe like in an industrial environment where data is collected, such asfrom sensors or instruments on, in or proximal to industrial machines orfrom data storage in the environment) to a receiver (e.g., another datacollector (such as in a swarm or coordinated group), instrumentationsystem, computer, storage, or the like in the industrial environment, orto a remote computer, server, cloud platform, database, data pool, datamarketplace, mobile device (e.g., mobile phone, personal computer,tablet, or the like), or other network-connected device of system), suchas via one or more network infrastructure elements (referred to in somecases herein as nodes), such as access points, switches, routers,servers, gateways, bridges, connectors, physical interfaces and thelike, using one or more network protocols, such as IP-based protocols,TCP/IP, UDP, HTTP, Bluetooth, Bluetooth Low Energy, cellular protocols,LTE, 2G, 3G, 4G, 5G, CDMA, TDSM, packet-based protocols, streamingprotocols, file transfer protocols, broadcast protocols, multi-castprotocols, unicast protocols, and others. For situations involvingbi-directional communication, any of the above-referenced devices orsystems, or others mentioned throughout this disclosure, may play therole of sender or receiver, or both. Network coding may account foravailability of networks, including the availability of multiplealternative networks, such that a transmission may be delivered acrossdifferent networks, either separated into different components orsending the same components redundantly. Network coding may account forbandwidth and spectrum availability; for example, a given spectrum maybe divided (such as with sub-dividing spectrum by frequency, bytime-division multiplexing, and other techniques). Networks orcomponents thereof may be virtualized, such as for purposes ofprovisioning of network resources, specification of network coding for avirtualized network, or the like. Network coding may include a widevariety of approaches as described in Appendix A, and in connection withFigures in Appendix A.

In embodiments, one or more network coding systems or methods of thepresent disclosure may use self-organization, such as to configurenetwork coding parameters for one or more transmissions over one or morenetworks using an expert system, which may comprise a model-based system(such as automatically selecting network coding parameters orconfiguration based on one or more defined or measured parametersrelating to a transmission, such as parameters of the data or content tobe transmitted, the sender, the receiver, the available networkinfrastructure components, the conditions of the network infrastructure,the conditions of the industrial environment, or the like). A model may,for example, account for parameters relating to file size, numbers ofpackets, size of a stream, criticality of a data packet or stream, valueof a packet or stream, cost of transmission, reliability of atransmission, quality of service, quality of transmission, quality ofuser experience, financial yield, availability of spectrum, input/outputspeed, storage availability, storage reliability, and many others asnoted throughout this disclosure. In embodiments, the expert system maycomprise a rule-based system, where one or more rules is executed basedon detection of a condition or parameter, calculation of a variable, orthe like, such as based on any of the above-noted parameters. Inembodiments, the expert system may comprise a machine learning system,such as a deep learning system, such as based on a neural network, aself-organizing map, or other artificial intelligence approach(including any noted throughout this disclosure or the documentsincorporated by reference). A machine learning system in any of theembodiments of this disclosure may configure one or more inputs,weights, connections, functions (including functions of individualneurons or groups of neurons in a neural net) or other parameters of anartificial intelligence system. Such configuration may occur withiteration and feedback, optionally involving human supervision, such asby feeding back various metrics of success or failure. In the case ofnetwork coding, configuration may involve setting one or more codingparameters for a network coding specification or plan, such asparameters for selection of a network, selection one or more nodes,selection of data path, configuration of timers or timing parameters,configuration of redundancy parameters, configuration of coding types(including use of regenerating codes, such as for use of network codingfor distributed storage, such as in peer-to-peer networks, such as apeer-to-peer network of data collectors, or a storage network for adistributed ledger, as noted elsewhere in this disclosure), coefficientsfor coding (including linear algebraic coefficients), parameters forrandom or near-random linear network coding (including generation ofnear random coefficients for coding), session configuration parameters,or other parameters noted in the network coding embodiments describedbelow, throughout this disclosure, and in the documents incorporatedherein by reference. For example, a machine learning system mayconfigure the selection of a protocol for a transmission, the selectionof what network(s) will be used, the selection of one or more senders,the selection of one or more routes, the configuration of one or morenetwork infrastructure nodes, the selection of a destination receiver,the configuration of a receiver, and the like. In embodiments, each oneof these may be configured by an individual machine learning system, orthe same system may configure an overall configuration by adjustingvarious parameters of one or more of the above under iteration, througha series of trials, optionally seeded by a training set, which may bebased on human configuration of parameters, or by model-based and/orrule-based configuration. Feedback to a machine learning system maycomprise various measures, including transmission success or failure,reliability, efficiency (including cost-based, energy-based and othermeasures of efficiency, such as measuring energy per bit transmitted,energy per bit stored, or the like), quality of transmission, quality ofservice, financial yield, operational effectiveness, success atprediction, success at classification, and others. In embodiments, amachine learning system may configure network coding parameters bypredicting network behavior or characteristics and may learn to improveprediction using any of the techniques noted above. In embodiments, amachine learning system may configure network coding parameters byclassification of one or more network elements and/or one or morenetwork behaviors and may learn to improve classification, such as bytraining and iteration over time. Such machine-based prediction and/orclassification may be used for self-organization, including bymodel-based, rule-based, and machine learning-based configuration. Thus,self-organization of network coding may use or comprise variouscombinations or permutations of model-based systems, rule-based systems,and a variety of different machine-learning systems (includingclassification systems, prediction systems, and deep learning systems,among others).

As described in US patent application 2017/0013065, entitled“Cross-session network communication configuration,” network coding mayinvolve methods and systems for data communication over a data channelon a data path between a first node and a second node and may includemaintaining data characterizing one or more current or previous datacommunication connections traversing the data channel and initiating anew data communication connection between the first node and the secondnode including configuring the new data communication connection atleast in part according to the maintained data. The maintained data maycharacterize one or more data channels on one or more data paths betweenthe first node and the second node over which said one or more currentor previous data communication connections pass. The maintained data maycharacterize an error rate of the one or more data channels. Themaintained data may characterize a bandwidth of the one or more datachannels. The maintained data may characterize a round trip time of theone or more data channels. The maintained data may characterizecommunication protocol parameters of the one or more current or previousdata communication connections.

The communication protocol parameters may include one or more of acongestion window size, a block size, an interleaving factor, a portnumber, a pacing interval, a round trip time, and a timing variability.The communication protocol parameters may include two or more of acongestion window size, a block size, an interleaving factor, a portnumber, a pacing interval, a round trip time, and a timing variability.

The maintained data may characterize forward error correction parametersassociated with the one or more current or previous data communicationconnections. The forward error correction parameters may include a coderate. Initiating the new data communication connection may includeconfiguring the new data communication connection according to firstdata of the maintained data, the first data is maintained at the firstnode, and initiating the new data communication connection includesproviding the first data from the first node to the second node forconfiguring the new data communication connection.

Initiating the new data communication connection may include configuringthe new data communication connection according to first data of themaintained data, the first data is maintained at the first node, andinitiating the new data communication connection includes accessingfirst data at the first node for configuring the new data communicationconnection. Any one of these elements of maintained data, includingvarious parameters of communication protocol, error correctionparameters, connection parameters, and others, may be provided to theexpert system for supporting self-organization of network coding,including for execution of rules to set network coding parameters basedon the maintained data, for population of a model, or for configurationof parameters of a neural net or other artificial intelligence system.

Initiating the new data communication connection may include configuringthe new data communication connection according to first data of themaintained data, the first data being maintained at the first node, andinitiating the new data communication connection includes accepting arequest from the first node for establishing the new data communicationconnection between the first node and the second node, includingreceiving, at the second node, at least one message from the first nodecomprising the first data for configuring said connection. The methodmay include maintaining the new data communication connection betweenthe first node and the second node, including maintaining communicationparameters, including initializing said communication parametersaccording the first data received in the at least one message from thefirst node.

Maintaining the new data communication connection may include adaptingthe communication parameters according to feedback from the first node.The feedback from the first node may include feedback messages receivedfrom the first node. The feedback may include feedback derived from aplurality of feedback messages received from the first node. Feedbackmay relate to any of the types of feedback noted above, and may be usedfor self-organizing the data communication connection using the expertsystem.

In some examples, one or more training communication connections over adata channel on a data path are employed prior to establishment of datacommunication connections over the data channel on the data path. Thetraining communication connections are used to collect information aboutthe data channel which is then used when establishing the datacommunication connections. In other examples, no training communicationconnections are employed and information about the data channel isobtained from one or more previous or current data communicationconnection over the data channel on the data path.

The present disclosure describes a method for data communication over adata channel on a data path between a first node and a second node, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include maintaining data characterizing one or morecurrent or previous data communication connections traversing the datachannel, and initiating a new data communication connection between thefirst node and the second node including configuring the new datacommunication connection at least in part according to the maintaineddata. In embodiments, the configuration of the new data communicationconnection is configured by an expert system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system uses atleast one of a rule and a model to set a parameter of the configuration.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is a machinelearning system that iteratively configures at least one of a set ofinputs, a set of weights, and a set of functions based on feedbackrelating to the data channel.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system takes aplurality of inputs from a data collector that accepts data about amachine operating in an industrial environment.

As described in US patent application 2017/0012861, entitled “Multi-pathnetwork communication,” self-organized network coding under control ofan expert system may involve methods and systems for data communicationbetween a first node and a second node over a number of data pathscoupling the first node and the second node and may include transmittingmessages between the first node and the second node over the number ofdata paths, including transmitting a first subset of the messages over afirst data path of the number of data paths and transmitting a secondsubset of the messages over a second data path of the number of datapaths. In situations where the first data path has a first latency andthe second data path has a second latency substantially larger than thefirst latency, and messages of the first subset of the messages arechosen to have first message characteristics and messages of the secondsubset are chosen to have second message characteristics, different fromthe first message characteristics.

Messages having the first message characteristics, targeted for datapaths of lower latency, may include time critical messages; for example,in an industrial environment, messages relating to a critical faultcondition of a machine (e.g., overheating, excessive vibration, or anyof the other fault conditions described throughout this disclosure) orrelating to a safety hazard, or a time-critical operational step onwhich other processes depend (e.g., completion of a catalytic reaction,completion of a sub-assembly, or the like in a high-value, high-speedmanufacturing process, a refining process, or the like) may bedesignated as time critical (such as by a rule that can be parsed orprocessed by a rules engine) or may be learned to be time-critical bythe expert system, such as based on feedback regarding outcomes overtime, including outcomes for similar machines having similar data insimilar industrial environments. The first subset of the messages andthe second subset of the messages may be determined from a portion ofthe messages available at the first node at a time of transmission. At asubsequent time of transmission, additional messages made available tothe first node may be divided into the first subset and the secondsubset based on message characteristics associated with the additionalmessages. Division into subsets and selection of what subsets aretargeted to what data path may be undertaken by an expert system.Messages having the first message characteristics may be associated withan initial subset of a data set and messages having the second messagecharacteristics may be associated with a subsequent subset of the dataset. The methods and systems described herein for selecting inputs fordata collection and for multiplexing data may be organized, such as byan expert system, to configure inputs for the alternative channels, suchas by providing streaming elements that have real-time significance tothe first data path and providing other elements, such as for long-term,predictive maintenance, to the other data path. In embodiments, themessages of the second subset may include messages that are at most nmessages ahead of a last acknowledged message in a sequentialtransmission order associated with the messages. In embodiments, n isdetermined based on a buffer size at one of the first and second nodes.

Messages having the first message characteristics may includeacknowledgement messages and messages having the second messagecharacteristics may include data messages. Messages having the firstmessage characteristics may include supplemental data messages. Thesupplemental data messages may include data messages may includeredundancy data and messages having the second message characteristicsmay include original data messages. The first data path may include aterrestrial data path and the second data path may include a satellitedata path. The terrestrial data path may include one or more of acellular data path, a digital subscriber line (DSL) data path, a fiberoptic data path, a cable internet based data path, and a wireless localarea network data path. The satellite data path may include one or moreof a low earth orbit satellite data path, a medium earth orbit satellitedata path, and a geostationary earth orbit satellite data path. Thefirst data path may include a medium earth orbit satellite data path ora low earth orbit satellite data path and the second data path mayinclude a geostationary orbit satellite data path.

The method may further include, for each path of the number of datapaths, maintaining an indication of successful and unsuccessful deliveryof the messages over the data path and adjusting a congestion window forthe data path based on the indication, which may occur under control ofan expert system, including based on feedback of outcomes of a set oftransmissions. The method may further include, for each path of thenumber of data paths, maintaining, at the first node, an indication ofwhether a number of messages received at the second node is sufficientto decode data associated with the messages. In embodiments, theindication is based on feedback received at the first node over thenumber of data paths.

In another general aspect, a system for data communication between anumber of nodes over a number of data paths coupling the number of nodesincludes a first node configured to transmit messages to a second nodeover the number of data paths including transmitting a first subset ofthe messages over a first data path of the number of data paths, andtransmitting a second subset of the messages over a second data path ofthe number of data paths.

In embodiments, the first subset of the messages and the second subsetof the messages for the respective data paths may be determined from aportion of the messages available at a first node at a time oftransmission. At a subsequent time of transmission, additional messagesmade available to the first node may be divided into a first subset anda second subset based on message characteristics associated with theadditional messages. Messages having the first message characteristicsmay be associated with an initial subset of a data set and messageshaving the second message characteristics may be associated with asubsequent subset of the data set.

In embodiments, the messages of the second subset may include messagesthat are at most n messages ahead of a last acknowledged message in asequential transmission order associated with the messages. Inembodiments, n is determined based on a receive buffer size at thesecond node. Messages having the first message characteristics mayinclude acknowledgement messages and messages having the second messagecharacteristics may include data messages. Messages having the firstmessage characteristics may include supplemental data messages. Thesupplemental data messages may include data messages includingredundancy data and messages having the second message characteristicsmay include original data messages.

The first node may be further configured to, for each path of the numberof data paths, maintain an indication of successful and unsuccessfuldelivery of the messages over the data path and adjust a congestionwindow for the data path based on the indication. The first node may befurther configured to maintain an aggregate indication of whether anumber of messages received at the second node over the number of datapaths is sufficient to decode data associated with the messages and totransmit supplemental messages based on the aggregate indication. Inembodiments, the aggregate indication is based on feedback from thesecond node received at the first node over the number of data paths.

The present disclosure describes a method for data communication betweena first node and a second node over a plurality of data paths couplingthe first node and the second node, the method according to onedisclosed non-limiting embodiment of the present disclosure can includetransmitting messages between the first node and the second node overthe plurality of data paths including transmitting a first subset of themessages over a first data path of the plurality of data paths, andtransmitting a second subset of the messages over a second data path ofthe plurality of data paths. In embodiments, the first data path has afirst latency and the second data path has a second latencysubstantially larger than the first latency, and messages of the firstsubset of the messages are chosen to have first message characteristicsand messages of the second subset are chosen to have second messagecharacteristics, different from the first message characteristics. Inembodiments, the selection of the first and second subset of messagecharacteristics is performed automatically under control of an expertsystem.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system uses atleast one of a rule and a model to set a parameter of the selection.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is a machinelearning system that iteratively configures at least one of a set ofinputs, a set of weights, and a set of functions based on feedbackrelating to at least one of the data paths.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system takes aplurality of inputs from a data collector that accepts data about amachine operating in an industrial environment.

As described in US patent application 2017/0012868, entitled “Multipleprotocol network communication,” self-organized network coding undercontrol of an expert system may involve methods and systems for datacommunication between a first node and a second node over one or moredata paths coupling the first node and the second node and may includetransmitting messages between the first node and the second node overthe data paths, including transmitting at least some of the messagesover a first data path using a first communication protocol,transmitting at least some of the messages over a second data path usinga second communication protocol, determining that the first data path isaltering a flow of messages over the first data path due to the messagesbeing transmitted using the first communication protocol, and inresponse to the determining, adjusting a number of messages sent overthe data paths, including decreasing a number of the messagestransmitted over the first data path and increasing a number of messagestransmitted over the second data path. Determination that the first datapath is altering a flow of messages and/or adjusting the number ofmessages sent over the data paths may occur under control of an expertsystem, such as a rule-based system, a model-based system, a machinelearning system (including deep learning) or a hybrid of any of those,where the expert system takes inputs relating to one or more of the datapaths, the nodes, the communication protocols used, or the like. Thedata paths may be among devices and systems in an industrialenvironment, such as instrumentation systems of industrial machines, oneor more mobile data collectors (optionally coordinated in a swarm), datastorage systems (including network-attached storage), servers and otherinformation technology elements, any of which may have or be associatedwith one or more network nodes. The data paths may be among any suchdevices and systems and devices and systems in a network of any kind(such as switches, routers, and the like) or between those and oneslocated in a remote environment, such as in an enterprise's informationtechnology system, in a cloud platform, or the like.

Determining that the first data path is altering the flow of messagesover the first data path may include determining that the first datapath is limiting a rate of messages transmitted using the firstcommunication protocol. Determining that the first data path is alteringthe flow of messages over the first data path may include determiningthat the first data path is dropping messages transmitted using thefirst communication protocol at a higher rate than a rate at which thesecond data path is dropping messages transmitted using the secondcommunication protocol. The first communication protocol may be the UserDatagram Protocol (UDP), and the second communication protocol may bethe Transmission Control Protocol (TCP), or vice versa. Other protocolsas described throughout this disclosure may be used.

The messages may be initially equally divided or divided according tosome predetermined allocation (such as by type, as noted in connectionwith other embodiments) across the first data path and the second datapath, such as using a load balancing technique. The messages may beinitially divided across the first data path and the second data pathaccording to a division of the messages across the first data path andthe second data path in one or more prior data communicationconnections. The messages may be initially divided across the first datapath and the second data path based on a probability that the first datapath will alter a flow of messages over the first data path due to themessages being transmitted using the first communication protocol.

The messages may be divided across the first data path and the seconddata path based on message type. The message type may include one ormore of acknowledgement messages, forward error correction messages,retransmission messages, and original data messages. Decreasing a numberof the messages transmitted over the first data path and increasing anumber of messages transmitted over the second data path may includesending all of the messages over the second path and sending none of themessages over the first path.

At least some of the number of data paths may share a common physicaldata path. The first data path and the second data path may share acommon physical data path. The adjusting of the number of messages sentover the number of data paths may occur during an initial phase of thetransmission of the messages. The adjusting of the number of messagessent over the number of data paths may repeatedly occur over a durationof the transmission of the messages. The adjusting of the number ofmessages sent over the number of data paths may include increasing anumber of the messages transmitted over the first data path anddecreasing a number of messages transmitted over the second data path.

In some examples, the parallel transmission over TCP and UDP is handleddifferently from conventional load balancing techniques, because TCP andUDP both share a low-level data path and nevertheless have verydifferent protocol characteristics.

In some examples, approaches respond to instantaneous network behaviorand learn the network's data handling policy and state by probing forchanges. In an industrial environment, this may include learningpolicies relating to authorization to use aspects of a network; forexample, a SCADA system may allow a data path to be used only by alimited set of authorized users, services, or applications, because ofthe sensitivity of underlying machines or processes that are undercontrol (including remote control) via the SCADA system and concern overpotential for cyberattacks. Unlike conventional load-balancers, whichassume each data path is unique and does not affect the other,approaches may recognize that TCP and UDP share a low-level data pathand directly affect each other. Additionally, TCP provides in-orderdelivery and retransmits data (along with flow control, congestioncontrol, etc.) whereas UDP does not. This uniqueness requires additionallogic provided by the methods and systems disclosed herein that mayinclude mapping specific message types to each communication protocol,such as based at least in part on the different properties of theprotocols (e.g., expect longer jitter over TCP, expect out-of-orderdelivery on UDP). For example, the system may refrain from coding overpackets sent through TCP, since it is reliable, but may send forwarderror correction over UDP to add redundancy and save bandwidth. In someexamples, a larger ACK interval is used for ACKing TCP data.

By employing the techniques described herein, approaches distribute dataover TCP and UDP data paths to achieve optimal or near-optimalthroughput, such as in situations where a network provider's policiestreat UDP unfairly (as compared to conventional systems that simply useUDP if possible and fall back to TCP if not).

A method for data communication between a first node and a second nodeover a plurality of data paths coupling the first node and the secondnode, the method comprising: transmitting messages between the firstnode and the second node over the plurality of data paths includingtransmitting at least some of the messages over a first data path of theplurality of data paths using a first communication protocol, andtransmitting at least some of the messages over a second data path ofthe plurality of data paths using a second communication protocol;determining that the first data path is altering a flow of messages overthe first data path due to the messages being transmitted using thefirst communication protocol, and in response to the determining,adjusting a number of messages sent over the plurality of data pathsincluding decreasing a number of the messages transmitted over the firstdata path and increasing a number of messages transmitted over thesecond data path. In embodiments, altering the flow of messages isperformed automatically under control of an expert system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system uses atleast one of a rule and a model to set a parameter of the alteration ofthe flow.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is a machinelearning system that iteratively configures at least one of a set ofinputs, a set of weights, and a set of functions based on feedbackrelating to at least one of the data paths.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system takes aplurality of inputs from a data collector that accepts data about amachine operating in an industrial environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the first communicationprotocol is UDP.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the second communicationprotocol is TCP.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the messages are initiallydivided across the first data path and the second data path using a loadbalancing technique.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the messages are initiallydivided across the first data path and the second data path according toa division of the messages across the first data path and the seconddata path in one or more prior data communication connections.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the messages are initiallydivided across the first data path and the second data path based on aprobability that the first data path will alter a flow of messages overthe first data path due to the messages being transmitted using thefirst communication protocol.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the probability is determinedby an expert system.

As described in US patent application 2017/0012884, entitled “Messagereordering timers,” self-organized network coding under control of anexpert system may involve methods and systems for data communicationfrom a first node to a second node over a data channel coupling thefirst node and the second node and may include receiving data messagesat the second node, the messages belonging to a set of data messagestransmitted in a sequential order from the first node, sending feedbackmessages from the second node to the first node, the feedback messagescharacterizing a delivery status of the set of data messages at thesecond node, including maintaining a set of one or more timers accordingto occurrences of a number of delivery order events, the maintainingincluding modifying a status of one or more timers of the set of timersbased on occurrences of the number of delivery order events, anddeferring sending of said feedback messages until expiry of one or moreof the set of one or more timers. The data channels may be among devicesand systems in an industrial environment, such as instrumentationsystems of industrial machines, one or more mobile data collectors(optionally coordinated in a swarm), data storage systems (includingnetwork-attached storage), servers and other information technologyelements, any of which may have or be associated with one or morenetwork nodes. The data channels may be among any such devices andsystems and devices and systems in a network of any kind (such asswitches, routers, and the like) or between those and ones located in aremote environment, such as in an enterprise's information technologysystem, in a cloud platform, or the like. Determination that timers arerequired, configuration of timers, and initiation of the user of timersmay occur under control of an expert system, such as a rule-basedsystem, a model-based system, a machine learning system (including deeplearning) or a hybrid of any of those, where the expert system takesinputs relating to one or more of the types of communications occurring,the data channels, the nodes, the communication protocols used, or thelike.

The set of one or more timers may include a first timer and the firsttimer may be started upon detection of a first delivery order event, thefirst delivery order event being associated with receipt of a first datamessage associated with a first position in the sequential order priorto receipt of one or more missing messages associated with positionspreceding the first position in the sequential order. The method mayinclude sending the feedback messages indicating a successful deliveryof the set of data messages at the second node upon detection of asecond delivery order event, the second delivery order event beingassociated with receipt of the one or more missing messages prior toexpiry of the first timer. The method may include sending said feedbackmessages indicating an unsuccessful delivery of the set of data messagesat the second node upon expiry of the first timer prior to any of theone or more missing messages being received. The set of one or moretimers may include a second timer and the second timer is started upondetection of a second delivery order event, the second delivery orderevent being associated with receipt of some but not all of the missingmessages prior to expiry of the first timer. The method may includesending feedback messages indicating an unsuccessful delivery of the setof data messages at the second node upon expiry of the second timerprior to receipt of the missing messages. The method may include sendingfeedback messages indicating a successful delivery of the set of datamessages at the second node upon detection of a third delivery orderevent, the third delivery order event being associated with receipt ofthe missing messages prior to expiry of the second timer.

In another general aspect, a method for data communication from a firstnode to a second node over a data channel coupling the first node andthe second node includes receiving, at the first node, feedback messagesindicative of a delivery status of a set of data messages transmitted ina sequential order to the second node from the second node, maintaininga size of a congestion window at the first node including maintaining aset of one or more timers according to occurrences of a number offeedback events, the maintaining including modifying a status of one ormore timers of the set of timers based on occurrences of the number offeedback events, and delaying modification of the size of the congestionwindow until expiry of one or more of the set of one or more timers.

The set of one or more timers may include a first timer and the firsttimer may be started upon detection of a first feedback event, the firstfeedback event being associated with receipt of a first feedback messageindicating successful delivery of a first data message having firstposition in the sequential order prior to receipt of one or morefeedback messages indicating successful delivery of one or more otherdata messages having positions preceding the first position in thesequential order. The method may include cancelling modification of thecongestion window upon detection of a second feedback event, the secondfeedback event being associated with receipt of one or more feedbackmessages indicating successful delivery of the one or more other datamessages prior to expiry of the first timer. The method may includemodifying the congestion window upon expiry of the first timer prior toreceipt of any feedback message indicating successful delivery of theone or more other data messages.

The set of one or more timers may include a second timer and the secondtimer may be started upon detection of a third feedback event, the thirdfeedback event being associated with receipt of one or more feedbackmessages indicating successful delivery of some but not all of the oneor more other data messages prior to expiry of the first timer. Themethod may include modifying the size of the congestion window uponexpiry of the second timer prior to receipt of one or more feedbackmessages indicating successful delivery of the one or more other datamessages. The method may include cancelling modification of the size ofthe congestion window upon detection of a fourth feedback event, thefourth feedback event being associated with receipt one or more feedbackmessages indicating successful delivery of the one or more other datamessages prior to expiry of the second timer.

In another general aspect, a system for data communication between anumber of nodes over a data channel coupling the number of nodesincludes a first node of the number of nodes configured to receive, atthe first node, feedback messages indicative of a delivery status of aset of data messages transmitted in a sequential order to the secondnode from the second node, maintain a size of a congestion window at thefirst node including maintaining a set of one or more timers accordingto occurrences of a number of feedback events, the maintaining includingmodifying a status of one or more timers of the set of timers based onoccurrences of the number of feedback events, and delaying modificationof the size of the congestion window until expiry of one or more of theset of one or more timers.

The present disclosure describes a method for data communication from afirst node to a second node over a data channel coupling the first nodeand the second node, the method according to one disclosed non-limitingembodiment of the present disclosure can include determining, using anexpert system, based on at least one condition of the data channel,whether one or more timers will be used to manage the data communicationand, upon such determination receiving data messages at the second node,the messages belonging to a set of data messages transmitted in asequential order from the first node, sending feedback messages from thesecond node to the first node, the feedback messages characterizing adelivery status of the set of data messages at the second node,including maintaining a set of one or more timers according tooccurrences of a plurality of delivery order events, the maintainingincluding modifying a status of one or more timers of the set of timersbased on occurrences of the plurality of delivery order events, anddeferring sending of said feedback messages until expiry of one or moreof the set of one or more timers.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system uses atleast one of a rule and a model to set a parameter of the determinationwhether to use one or more timers.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is a machinelearning system that iteratively configures at least one of a set ofinputs, a set of weights, and a set of functions based on feedbackrelating to at least one of the data paths.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system takes aplurality of inputs from a data collector that accepts data about amachine operating in an industrial environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the set of one or more timersincludes a first timer and the first timer is started upon detection ofa first delivery order event, the first delivery order event beingassociated with receipt of a first data message associated with a firstposition in the sequential order prior to receipt of one or more missingmessages associated with positions preceding the first position in thesequential order.

As described in US patent application 2017/0012885, entitled, “NetworkCommunication Recoding Node,” self-organized network coding undercontrol of an expert system may involve methods and systems formodifying redundancy information associated with encoded data passingfrom a first node to a second node over data paths and may includereceiving first encoded data including first redundancy information atan intermediate node from the first node via a first channel connectingthe first node and the intermediate node, the first channel having firstchannel characteristics, and transmitting second encoded data includingsecond redundancy information from the intermediate node to the secondnode via a second channel connecting the intermediate node and thesecond node, the second channel having second channel characteristics. Adegree of redundancy associated with the second redundancy informationmay be determined by modifying the first redundancy information based onone or both of the first channel characteristics and the second channelcharacteristics without decoding the first encoded data. The data pathsmay be among devices and systems in an industrial environment (eachacting as one or more nodes for sending, receiving, or transmittingdata), such as instrumentation systems of industrial machines, one ormore mobile data collectors (optionally coordinated in a swarm), datastorage systems (including network-attached storage), servers and otherinformation technology elements, any of which may have or be associatedwith one or more network nodes. The data paths may be among any suchdevices and systems and devices and systems in a network of any kind(such as switches, routers, and the like) or between those and oneslocated in a remote environment, such as in an enterprise's informationtechnology system, in a cloud platform, or the like. Modifying theredundancy information may occur by or under control of an expertsystem, such as a rule-based system, a model-based system, a machinelearning system (including deep learning) or a hybrid of any of those,where the expert system takes inputs relating to one or more of the datapaths, the nodes, the communication protocols used, or the like.Redundancy may result from (and may be identified at least in part basedon), the combination or multiplexing of data from a set of data inputs,such as described throughout this disclosure.

Modifying the first redundancy information may include adding redundancyinformation to the first redundancy information. Modifying the firstredundancy information may include removing redundancy information fromthe first redundancy information. The second redundancy information maybe further formed by modifying the first redundancy information based onfeedback from the second node indicative of successful or unsuccessfuldelivery of the encoded data to the second node. The first encoded dataand the second encoded data may be encoded, such as using a randomlinear network code or a substantially random linear network code.Modifying the first redundancy information based on one or both of thefirst channel characteristics and the second channel characteristics mayinclude modifying the first redundancy information based on one or moreof a block size, a congestion window size, and a pacing rate associatedwith the first channel characteristics and/or the second channelcharacteristics.

The method may include sending a feedback message from the intermediatenode to the first node acknowledging receipt of one or more messages atthe intermediate node. The method may include receiving a feedbackmessage from the second node at the intermediate node and, in responseto receiving the feedback message, transmitting additional redundancyinformation to the second node.

In another general aspect, a system for modifying redundancy informationassociated with encoded data passing from a first node to a second nodeover a number of data paths includes an intermediate node configured toreceive first encoded data including first redundancy information fromthe first node via a first channel connecting the first node and theintermediate node, the first channel having first channelcharacteristics and transmit second encoded data including secondredundancy information from the intermediate node to the second node viaa second channel connecting the intermediate node and the second node,the second channel having second channel characteristics. A degree ofredundancy associated with the second redundancy information isdetermined by modifying the first redundancy information based on one orboth of the first channel characteristics and the second channelcharacteristics without decoding the first encoded data.

The present disclosure describes a method for modifying redundancyinformation associated with encoded data passing from a first node to asecond node over a plurality of data paths, the method according to onedisclosed non-limiting embodiment of the present disclosure can includereceiving first encoded data including first redundancy information atan intermediate node from the first node via a first channel connectingthe first node and the intermediate node, the first channel having firstchannel characteristics, transmitting second encoded data includingsecond redundancy information from the intermediate node to the secondnode via a second channel connecting the intermediate node and thesecond node, the second channel having second channel characteristics.In embodiments, a degree of redundancy associated with the secondredundancy information is determined by modifying the first redundancyinformation based on one or both of the first channel characteristicsand the second channel characteristics without decoding the firstencoded data, including modifying the first redundancy information basedon one or more of a block size, a congestion window size, and a pacingrate associated with the first channel characteristics and/or the secondchannel characteristics. In embodiments, modifying the first redundancyinformation occurs under control of an expert system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system uses atleast one of a rule and a model to set a parameter of the modificationof the redundancy information.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is a machinelearning system that iteratively configures at least one of a set ofinputs, a set of weights, and a set of functions based on feedbackrelating to at least one of the data paths.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system takes aplurality of inputs from a data collector that accepts data about amachine operating in an industrial environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein modifying the first redundancyinformation includes adding redundancy information to the firstredundancy information.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein modifying the first redundancyinformation includes removing redundancy information from the firstredundancy information.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the second redundancyinformation is further formed by modifying the first redundancyinformation based on feedback from the second node indicative ofsuccessful or unsuccessful delivery of the encoded data to the secondnode.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the first encoded data and thesecond encoded data are encoded using a random linear network code.

As described in US patent application 2017/0012905, entitled “Errorcorrection optimization,” self-organized network coding under control ofan expert system may involve methods and systems for data communicationbetween a first node and a second node over a data path coupling thefirst node and the second node and may include transmitting a segment ofdata from the first node to the second node over the data path as anumber of messages, the number of messages being transmitted accordingto a transmission order. A degree of redundancy associated with eachmessage of the number of messages is determined based on a position ofsaid message in the transmission order. The data paths may be amongdevices and systems in an industrial environment (each acting as one ormore nodes for sending, receiving, or transmitting data), such asinstrumentation systems of industrial machines, one or more mobile datacollectors (optionally coordinated in a swarm), data storage systems(including network-attached storage), servers and other informationtechnology elements, any of which may have or be associated with one ormore network nodes. The data paths may be among any such devices andsystems and devices and systems in a network of any kind (such asswitches, routers, and the like) or between those and ones located in aremote environment, such as in an enterprise's information technologysystem, in a cloud platform, or the like. Determining a transmissionorder may occur by or under control of an expert system, such as arule-based system, a model-based system, a machine learning system(including deep learning) or a hybrid of any of those, where the expertsystem takes inputs relating to one or more of the data paths, thenodes, the communication protocols used, or the like. Redundancy mayresult from (and may be identified at least in part based on), thecombination or multiplexing of data from a set of data inputs, such asdescribed throughout this disclosure.

The degree of redundancy associated with each message of the number ofmessages may increase as the position of the message in the transmissionorder is non-decreasing. Determining the degree of redundancy associatedwith each message of the number of messages based on the position (i) ofthe message in the transmission order is further based on one or more ofdelay requirements for an application at the second node, a round triptime associated with the data path, a smoothed loss rate (P) associatedwith the channel, a size (N) of the data associated with the number ofmessages, a number (ai) of acknowledgement messages received from thesecond node corresponding to messages from the number of messages, anumber (fi) of in-flight messages of the number of messages, and anincreasing function (g(i)) based on the index of the data associatedwith the number of messages.

The degree of redundancy associated with each message of the number ofmessages may be defined as: (N+g(i)−ai)/(1−p)−fi. g(i) may be defined asa maximum of a parameter m and N−i. g(i) may be defined as N−p(i) wherep is a polynomial, with integer rounding as needed. The method mayinclude receiving, at the first node, a feedback message from the secondnode indicating a missing message at the second node and, in response toreceiving the feedback message, sending a redundancy message to thesecond node to increase a degree of redundancy associated with themissing message. The method may include maintaining, at the first node,a queue of preemptively computed redundancy messages and, in response toreceiving the feedback message, removing some or all of the preemptivelycomputed redundancy messages from the queue and adding the redundancymessage to the queue for transmission. The redundancy message may begenerated and sent on-the-fly in response to receipt of the feedbackmessage.

The method may include maintaining, at the first node, a queue ofpreemptively computed redundancy messages for the number of messagesand, in response to receiving a feedback message indicating successfuldelivery of the number of messages, removing any preemptively computedredundancy messages associated with the number of messages from thequeue of preemptively computed redundancy messages. The degree ofredundancy associated with each of the messages may characterize aprobability of correctability of an erasure of the message. Theprobability of correctability may depend on a comparison of between thedegree of redundancy and a loss probability.

The present disclosure describes a method for data communication betweena first node and a second node over a data path coupling the first nodeand the second nod, the method according to one disclosed non-limitingembodiment of the present disclosure can include transmitting a segmentof data from the first node to the second node over the data path as aplurality of messages, the plurality of messages being transmittedaccording to a transmission order. In embodiments, a degree ofredundancy associated with each message of the plurality of messages isdetermined based on a position of said message in the transmissionorder. In embodiments, the transmission order is determined undercontrol of an expert system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system uses atleast one of a rule and a model to set a parameter of the transmissionorder.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is a machinelearning system that iteratively configures at least one of a set ofinputs, a set of weights, and a set of functions based on feedbackrelating to at least one of the data paths.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system takes aplurality of inputs from a data collector that accepts data about amachine operating in an industrial environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the degree of redundancyassociated with each message of the plurality of messages increases asthe position of the message in the transmission order is non-decreasing.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the degree ofredundancy associated with each message of the plurality of messagesbased on the position (i) of the message in the transmission order isfurther based on one or more of application delay requirements, a roundtrip time associated with the data path, a smoothed loss rate (P)associated with the channel, a size (N) of the data associated with theplurality of messages, a number (ai) of acknowledgement messagesreceived from the second node corresponding to messages from theplurality of messages, a number (fi) of in-flight messages of theplurality of messages, and an increasing function (g(i)) based on theindex of the data associated with the plurality of messages.

As described in U.S. patent application Ser. No. 14/935,885, entitled,“Packet Coding Based Network Communication,” self-organized networkcoding under control of an expert system may involve methods and systemsfor data communication between a first node and a second node over apath and may include estimating a rate at which loss events occur, wherea loss event is either an unsuccessful delivery of a single packet tothe second data node or an unsuccessful delivery of a plurality ofconsecutively transmitted packets to the second data node, and sendingredundancy messages at the estimated rate at which loss events occur. Anexpert system may be used to estimate the rate at which loss eventsoccur.

A method for data communication from a first node to a second node overa data channel coupling the first node and the second node such as in anindustrial environment, includes receiving messages at the first node,from the second node, including receiving messages comprising data thatdepend at least in part of characteristics of the channel coupling thefirst node and the second node, transmitting messages from the firstnode to the second node, including applying forward error correctionaccording to parameters determined from the received messages, theparameters determined from the received messages including at least twoof a block size, an interleaving factor, and a code rate. The method mayoccur under control of an expert system.

The present disclosure describes a method for data communication from afirst node in an industrial environment to a second node over a datachannel coupling the first node and the second node, the methodaccording to one disclosed non-limiting embodiment of the presentdisclosure can include receiving messages at the first node from thesecond node, including receiving messages including data that depend atleast in part of characteristics of the channel coupling the first nodeand the second node, transmitting messages from the first node to thesecond node, including applying error correction according to parametersdetermined from the received messages, the parameters determined fromthe received messages including at least two of a block size, aninterleaving factor, and a code rate. In embodiments, applying the errorcorrection occurs under control of an expert system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system uses atleast one of a rule and a model to set a parameter of the errorcorrection.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is a machinelearning system that iteratively configures at least one of a set ofinputs, a set of weights, and a set of functions based on feedbackrelating to at least one of the data paths.

As depicted in FIG. 180, a cloud platform for supporting deployments ofdevices in the IoT, such as within industrial environments, may includevarious components, modules, services, elements, applications,interfaces, and other elements (collectively referred to as the “cloudplatform 13000”), which may include a policy automation engine 13002 anda data marketplace 13008. The cloud platform 13000 may include,integrate with, or connect to various devices 13006, a cloud computingenvironment 13068, data pools 13070, data collectors 13020 and sensors13024. The cloud platform 13000 may also include systems andcapabilities for self-organization 13012, machine learning 13014 andrights management 13016.

Within the cloud platform 13000, various components may be deployed in awide range of architectures and arrangements. In embodiments, thedevices 13006 may connect to, integrate with, or be deployed within thecloud computing environment 13068, the policy automation engine 13002,the data marketplace 13008, the data collectors 13020, as well as thesystems and capabilities for self-organization 13012, the machinelearning 13014 and the rights management 13016. Devices 13006 mayconnect to or integrate with the policy automation engine 13002, thedata marketplace 13008, the data collectors 13020 and the systems orcapabilities for self-organization 13012, the machine learning 13014 andthe rights management 13016, either directly or through the cloudcomputing environment 13068.

The devices 13006 may be IoT devices, including IoT devices, such as forcollecting, exchanging and managing information relating to machines,personnel, equipment, infrastructure elements, components, parts,inventory, assets, and other features of a wide range of industrialenvironments, such as those described throughout this disclosure. Thedevices 13006 may also connect via various protocols 13004, such asnetworking protocols, streaming protocols, file transfer protocols, datatransformation protocols, software operating system protocols, and thelike. Devices may connect to the policy automation engine 13002, such asfor executing policies that may be deployed within the cloud platform13000, such as governing activities, permissions, rules, and the likewithin the cloud platform 13000. The devices 13006 may also connect todata streams 13010 within the data marketplace 13008.

The data pools 13070 may connect to or integrate with the cloudcomputing environment 13068, the data collectors 13020 and the datamarketplace 13008, the policy automation engine 13002, theself-organization 13012, the machine learning 13014 and the rightsmanagement 13016 capabilities. The data pools 13070 may be includedwithin the cloud computing environment 30 or be external to the cloudcomputing environment 13068. As a result, connections to the data pools13070 may be made directly to the data pools 13070, through cloudconnections to the data pools 13070 or through a combination of directand cloud connections to the data pools 13070. The data pools 13070 mayalso be included within the data marketplace 13008 or external to thedata marketplace 13008.

The data pools 13070 may include a multiplexer (MUX) 13022 and alsoconnect to the self-organization 13012, the machine learning 13014 andthe rights management capabilities. The MUX 13022 may connect to thesensors 13024, collect data from the sensors 13024 and integrate datacollected from the sensors 13024 into a single set of data. In anexemplary and non-limiting embodiment, the data pools 13070, the datacollectors 13020 and the sensors 13024 may be included within anindustrial environment 13018.

The policy automation engine 13002 and the data marketplace 13008 may beused in a variety of industrial environments 13018. Industrialenvironments 13018 may include aerospace environments, agricultureenvironment, assembly line environments, automotive environments, andchemical and pharmaceutical environments. Industrial environments 13018may also include food processing environments, industrial componentenvironments, mining environments, oil and gas environments,particularly oil and gas production environments, truck and carenvironments and the like.

Similarly, the devices 13006 may include a variety of devices that mayoperate within the industrial environments or that may collect data withrespect to other such devices. Among many examples, the devices 13006may include agitators, including turbine agitators, airframe controlsurface vibration devices, catalytic reactors and compressors. Thedevices 13006 may also include conveyors and lifters, disposal systems,drive trains, fans, irrigation systems and motors. The devices 13006 mayalso include pipelines, electric powertrains, production platforms,pumps, such as water pumps, robotic assembly systems, thermic heatingsystems, tracks, transmission systems and turbines. The devices 13006may operate within a single industrial environment 13018 or multipleindustrial environments 13018. For example, a pipeline device mayoperate within an oil and gas environment, while a catalytic reactor mayoperate in either an oil and gas production environment or apharmaceutical environment.

The policy automation engine 13002 may be a cloud-based policyautomation engine 13002. The policy automation engine 13002 may be usedto create, deploy, and/or manage an interconnected set of policies13030, rules 13028 and the protocols 13004, such as policies relating tosecurity, authorization, permissions, and the like. For example,policies may govern what users, applications, services, systems,devices, or the like may access an IoT device, may read data from an IoTdevice, may subscribe to a stream from an IoT device, may write data toan IoT device, may establish a network connection with an IoT device,may provision an IoT device, may collaborate with an IoT device, or thelike.

The policy automation engine 13002 may generate and manage the policies13030. The policy generation engine may be the centralized policymanagement system for the cloud platform 13000.

The policies 13030 generated and managed by the policy automation engine13002 may deploy a large number of the rules 13028 to permit access toand use of different aspects of IoT devices. The policies 13030 mayinclude IoT device creation policies 13032, IoT device deploymentpolicies 13034, IoT device management policies 13036 and the like. Thepolicies 13030 may be communicated to the devices 13006 through theprotocols 13004 or directly from the policy automation engine 13002.

For example, in an exemplary and non-limiting embodiment, the policyautomation engine 13002 may manage the policies 13030 and create theprotocols 13004 that specify and enforce roles 13026 and the permissions13074 for workers, related to how the workers may use data provided byIoT devices. Workers may be human workers or machine workers.

In additional exemplary and non-limiting embodiments, the policies 13030may be used to automate remediation processes. Remediation processes maybe performed when a system is partially disabled, when equipment failsand when an entire system may be disabled. Remediation processes mayinclude instructions to initiate system restarts, bypass or replaceequipment, notify appropriate stakeholders of the condition and thelike. The policy automation engine 13002 may also include the policies13030 that specify the roles 13026 and the permissions 13074 requiredfor users 13072 to initiate or otherwise act upon the remediation orother processes.

The policy automation engine 13002 may also specify and detectconditions. Conditions may determine when the policies 13030 aredistributed or otherwise acted upon. Conditions may include individualconditions, sets of conditions, independent conditions, interdependentconditions, and the like.

In an exemplary and non-limiting embodiment of an independent condition,the policy automation engine 13002 may determine that the failure of anon-critical device 13006 does not require notification of the systemoperator. In an exemplary and non-limiting embodiment of aninterdependent set of conditions, the policy automation engine 13002 maydetermine that the failure of two non-critical system devices 13006 doesrequire notification of the system operator, as the failure of twonon-critical system devices 13006 may be an early indicator of apossible system-wide failure.

As depicted in FIG. 181, the policy automation engine 13002 may includecompliance policies 13050 and fault, configuration, accounting,provisioning, and security (FCAPS) policies 13052. The policies 13030may connect to the rules 13028, the protocols 13004 and policy inputs13048.

The policies 13030 may provide input to the rules 13028 and provideinformation related to how the roles 13026, permissions 13074 and uses130280 are defined. The policies 13030 may receive the policy inputs13048 and incorporate the policy inputs 13048 as policy parameters thatare included within the policies 13030. The policies 13030 may provideinputs to the protocols 13004 and be included within the protocols 13004that are used to create, deploy and manage the devices 13006.

The compliance policies 13050 may include data ownership policies, dataanalysis policies, data use policies, data format policies, datatransmission policies, data security policies, data privacy policies,information sharing policies, jurisdictional policies, and the like.Data transmission policies may include cross-jurisdictional datatransmission policies.

Data ownership policies may indicate the policies 13030 that manage whocontrols data, who can use data, how the data can be used and the like.Data analysis policies may indicate what data holders can do with datathat they are permitted to access, as well as determine what data theycan look at and what data may be combined with other data. For example,a data holder may look at aggregated user data but not individual userdata. Data use policies may indicate how data may be used and under whatcircumstances data may be used Data format policies may indicatestandard formats and mandated formats permitted for the handling ofdata. Data transmission policies, including cross-jurisdictional datatransmission policies, may determine the policies 13030 that specify howinter-jurisdictional and intra-jurisdictional transmission of data maybe handled. Data security policies may determine how data at rest, forexample stored data, as well transmitted data is required to be secured.

Data privacy policies may determine how data may or may not be shared,for example within an organization and external to an organization.Information sharing policies may determine how data may be sold, sharedand under what circumstances information can be sold and shared.Jurisdictional policies may determine who controls data, when and wherethe data may be controlled, for data within and transmitted acrossboundaries.

The FCAPS policies 13052 may include fault management policies,configuration management policies, accounting management policies,provisioning management policies, and security management policies.Fault management policies may specify policies 13030 used to handledevice faults. Configuration management policies may specify policiesused to configure the devices 13006. Accounting management policies mayspecify policies 13030 used for device accounting purposes, such asreporting, billing and the like. Provisioning management policies mayspecify policies 13030 used to provision services on the devices 13006.Security management policies may specify policies 13030 used to securethe devices 13006.

The policy inputs 13048 may be received from a policy input interface13046. The policy inputs 13048 may include standards-based policy inputs13044 and other the policy inputs 13048. Standards-based policy inputs13044 may include inputs related to standard data formats, standard rulesets and other standards-related information set by standards bodies,for example.

Other policy inputs 13048 may include a wide range of informationrelated industry-specific policies, cross-industry policies,manufacturer-specific policies, device-specific policies 13030 and thelike. The policy inputs 13048 may connect to the cloud computingenvironment 13068 and be provided through the policy input interface13046. The policy input interface 13046 may collect the policy inputs13048 provided by machines or entered by human operators.

As depicted in FIG. 180, the data marketplace 13008 may include the datastreams 13010, a data marketplace input interface, data marketplaceinputs 13056, a data payment allocation engine 13038, marketplace valuerating engine 13040, a data brokering engine 13042, a marketplaceself-organization engine 13076 and one or more data pools 13070. Thedata marketplace 13008 may be included within the cloud networkingenvironment 13068 or externally connected to the cloud networkingenvironment 13068. The data pools 13070 may also be included within thecloud networking environment 13068 or may be externally connected to thecloud networking environment 13068.

The data marketplace 13008 may connect to the data pools 13070 directly,for example if the data marketplace 13008 and the data pools 13070 arelocated in the same physical location. The data marketplace 13008 mayconnect to the data pools 13070 via the cloud networking environment 30,for example if the data marketplace 13008 and the data pools 13070 arelocated in different physical locations.

The data marketplace 13008 may connect to and receive inputs. The datamarketplace 13008 may receive marketplace inputs through datainterfaces, for example one or more the data collectors 13020. The datacollectors 13020 may be multiplexing data collectors. Inputs receivedthrough the data collectors 13020 may be received as one or more thanone of the data streams 13010 from one or more than one of the datacollectors 13020 and integrated into additional data streams 13010 bythe multiplexer 13022.

The data streams 13010 may also include data from the data pools 60.Data marketplace inputs, the data streams 13010 and the data pools 13070may include metrics and measures of success of the data marketplace13008. The metrics and measures of success of the data marketplace 13008may then be used by the machine learning capability 13014 to configureone or more parameters of the data marketplace 13008.

Inputs may be consortia inputs 13054. The consortia inputs 13054 may bereceived from consortia. Consortia may include energy consortia,healthcare consortia, manufacturing consortia, smart city consortia,transportation consortia and the like. Consortia may be pre-existingconsortia or new consortia.

In an exemplary and non-limiting embodiment, new consortia may be formedas a result of the data marketplace 13008 making available particulardata types and data combinations. The data brokering engine 13042 mayallow consortia members to trade information. The data brokering engine13042 may allow consortia members to trade information based oninformation value, as calculated by the marketplace value rating engine13040, for example.

The data marketplace 13008 may also connect to the self-organization13012, the machine learning 13014 and the rights management 13016capabilities. The rights management capabilities 13016 may includerights.

Rights may include business strategy and solution rights, liaison rights13058, marketing rights 13078, security rights 13060, technology rights13062, testbed rights 13064 and the like. Business strategy and solutionlifecycle rights may include business strategy and planning rights,industrial internet system design rights, project management rights,solution evaluation and contractual aspects rights. The liaison rights13058 may include standards organization rights, open-source communityrights, certification and testing body rights and governmentalorganization rights. The marketing rights 13078 may includecommunication rights, energy rights, healthcare rights,marketing-security rights, retail operation rights, smart factory rightsand thought leadership rights. The security rights 13060 may includedriving rights that drive industry consensus, promote security bestpractices and accelerate the adoption of security best practices.

The technology rights 13062 may include architecture rights,connectivity rights, distributed data management and interoperabilityrights, industrial analytics rights, innovation rights, IT/OT rights,safety rights, vocabulary rights, use case rights and the liaison rights13058. The testbed rights 13064 may include rights to implement ofspecific use cases and scenarios, as well as rights to produce testableoutcomes to confirm that an implementation conforms to expected results,for example. The testbed rights 13064 may also include rights to exploreuntested or existing technologies working together, for exampleinteroperability testing, generate new and potentially disruptiveproducts and services and generate requirements and priorities forstandards organizations, consortia and other stakeholder groups.

The rights management capability may assign different rights todifferent participants in the data marketplace 13008. In an exemplaryand non-limiting embodiment, manufacturers or remote maintenanceorganizations (RMOs). Participants may be assigned rights to informationbased on their equipment or proprietary methods. The data marketplace13008 may then ensure that only the appropriate data streams 13010 aremade available to the market, based on the assigned rights.

The rights management capability 13016 may manage permissions to accessthe data in the marketplace 13008. One or more parameters of the rightsmanagement capability 13016 may be automatically configured by themachine learning capability 13014 and may be based on a metric ofsuccess of the data marketplace 13008. The machine learning engine 13014may also use the metric and measure of success to configure a userinterface. The user interface may present a data element of the user ofthe data marketplace 13008. The user interface may also present one ormore mechanisms by which a user of the data marketplace 13008 may obtainaccess to one or more of the data elements.

The data payment allocation engine 13038 may allocate data marketplacepayments. The data payment allocation engine 13038 may allocate datamarketplace payments according to the value of the data stream 13010,the value of a contribution to the data stream 13010 and the like. Thistype of payment allocation may allow the data marketplace 13008 toallocate payments to data contributors, based on the value of the datacontributions.

For example, contributors of data to a higher-value data stream 13010may receive higher payments than contributors of data to lower-valuedata streams 13010. Similarly, data marketplace participants, forexample IoT device manufacturers and system integrators, may be rated orranked by the value of the data or the power of the configurations theyprovide and support.

The data marketplace 13008 may be a self-organizing data marketplace. Aself-organizing data marketplace may self-organize using theself-organization capabilities 13012. The self-organization capabilities13012 may be learned, developed and optimized using artificialintelligence (AI) capabilities. AI capabilities may be provided by themachine learning capability 13014, for example. Self-organization mayoccur via an expert system and may be based on the application of amodel, one or more rules, or the like. Self-organization may occur via aneural network or deep learning system, such as by optimizing variationsof the organization of the data pool over time based on feedback to oneor more measures of success. Self-organization may occur by a hybrid orcombination of a rule-based system, model-based system, and neuralnetwork or other AI system. Various capabilities may be self-organized,such as how data elements are presented in the user interface of themarketplace, what data elements are presented, what data streams areobtained as inputs to the marketplace, how data elements are described,what metadata is provided with data elements, how data elements arestored (such as in a cache or other “hot” storage or in slower, but lessexpensive storage locations), where data elements are stored (such as inedge elements of a network), how data elements are combined, fused ormultiplexed, or the like. Feedback to self-organization may includevarious metrics and measures of success, such as profit measures, yieldmeasures, ratings (such as by users, purchasers, licensees, reviewers,and the like), indicators of interest (such as clickstream activity,time spent on a page, time spent reviewing elements and links to dataelements), and others as described throughout this disclosure.

The data marketplace inputs 13056, the data streams 13010 and the datapools 13070 may be organized, based on metrics and measures of successof the data marketplace 13056. The data marketplace inputs 13056, thedata streams 13010 and the data pools 13070 may be organized by theself-organization capability 13012, allowing the marketplace inputs13056, the data streams 13010, and the data pools 13070 to be organizedautomatically, without requiring interaction by a user of the datamarketplace 13008.

The metric and measure of success may also be used to configure the databrokering engine 13042 to execute a transaction among at least twomarketplace participants. The machine learning engine 13014 may use themetric of success to configure the data brokering engine 13042automatically, without requiring user intervention. The metric ofsuccess may also be used by a pricing engine, for example themarketplace value rating engine 13040, to set the price of one or moredata elements within the data marketplace 13008.

In an exemplary and non-limiting embodiment, the self-organizing datamarketplace may self-organize to determine which type of the datastreams 13010 are the most valuable and offer the most valuable andother data streams 13010 for sale. The calculation of data stream valuemay be performed by the marketplace value rating engine 13040.

In embodiments, a policy automation system for a data collection systemin an industrial environment may comprise: a policy input interfacestructured to receive policy inputs relating to definition of at leastone parameter of at least one of a rule, a policy and a protocol. Inembodiments, the at least one parameter defines at least one of aconfiguration for a data collection device, an access policy foraccessing data from the data collection device, and collection policyfor collection of data by the device; and a policy automation engine fortaking the inputs and automatically configuring and deploying at leastone of the rule, the policy and the protocol within the system for datacollection. In embodiments, the at least one parameter may define atleast one of an energy utilization policy, a cost-based policy, a datawriting policy, and a data storage policy. The parameter may relate to apolicy selected from among compliance, fault, configuration, accounting,provisioning and security policies for defining how devices are created,deployed and managed. The compliance policies may include data ownershippolicies. The data ownership policies may specify who owns data. Thedata ownership policies may specify how owners may use data. Thecompliance policies may include data analysis policies. The dataanalysis policies may specify what data holders may access, how dataholders may use data, and how data may be combined with other data bydata holders. The compliance policies may include data use policies,data format policies, and the like. The data format policies may includestandard data format policies, mandated data format policies. Thecompliance policies may include data transmission policies. The datatransmission policies may include inter-jurisdictional transmission datatransmission policies. The compliance policies may include data securitypolicies, data privacy policies, information sharing policies, and thelike. The data security policies may include at rest data securitypolicies, transmitted data security policies, and the like. Theinformation sharing policies may include policies specifying wheninformation may be sold, when information may be shared, and the like.The compliance policies may include jurisdictional policies. Thejurisdictional policies may include policies specifying who controlsdata. The jurisdictional policies may include policies specifying whendata may be controlled. The jurisdictional policies may include policiesspecifying how data transmitted across boundaries is controlled.

In embodiments, a policy automation system for a data collection systemin an industrial environment may comprise: a policy automation enginefor enabling configuration of a plurality of policies applicable tocollection and utilization of data handled by a plurality of networkconnected devices deployed in a plurality of industrial environments. Inembodiments, the policy automation engine is hosted on informationtechnology infrastructure elements that are located separately from theindustrial environment. In embodiments, upon configuration of a policyin the policy automation engine, the policy is automatically deployedacross a plurality of devices in the plurality of industrialenvironments. In embodiments, the policy sets configuration parametersrelating to what data is collected by the data collection system andrelating to access permissions for the collected data. The policies mayinclude a plurality of policies selected among compliance, fault,configuration, accounting, provisioning and security policies fordefining how devices are created, deployed and managed, and theplurality of policies communicatively coupled to policies. A policyinput interface may be structured to receive policy inputs used as aninput to at least one of a rule, policy and protocol definition, such aswhere the policy automation system a centralized source of policies forcreating, deploying and managing policies for devices within anindustrial environment.

In embodiments, a policy automation system for a data collection systemin an industrial environment may comprise: a policy automation enginefor enabling configuration of a plurality of policies applicable tocollection and utilization of data handled by a plurality of networkconnected devices deployed in a plurality of industrial environments. Inembodiments, the policy automation engine is hosted on informationtechnology infrastructure elements that are located separately from theindustrial environment. In embodiments, upon configuration of a policyin the policy automation engine, the policy is automatically deployedacross a plurality of devices in the plurality of industrialenvironments. In embodiments, the policy sets configuration parametersrelating to what data is collected by the data collection system andrelating to access permissions for the collected data. In embodiments,the policy automation system is communicatively coupled to a pluralityof devices through a cloud network connection. The cloud networkconnection may be a privately-owned cloud connection, a publiclyprovided cloud connection, a publicly provided cloud connection, theprimary connection between the policy automation system and device, theprimary connection between the policy automation system and device, anintranet cloud connection, connecting devices within a singleenterprise, an extranet cloud connection, connecting devices amongmultiple enterprises, a secure cloud network connection, secured by avirtual private network (VPN) connection, and the like.

In embodiments, a data marketplace for a data collection system in anindustrial environment may comprise: an input interface structured toreceive marketplace inputs; at least one of a data pool and a datastream to provide collected data within the marketplace; and datastreams that include data from data pools. In embodiments, at least oneparameter of the marketplace may be automatically configured by amachine learning facility based on a metric of success of themarketplace. The inputs may include a plurality of data streams from aplurality of industrial data collectors. The data collectors may bemultiplexing data collectors. The inputs may include consortia inputs. Aconsortium may be an existing consortium, a new consortium, a newconsortium related to a data stream through a common interest, and thelike. The metrics and measures of success may include profit measures,yield measures, ratings, indicators of interest, and the like. Theratings may include user ratings, purchaser ratings, licensee ratings,reviewer ratings, and the like. The indicators of interest may includeclickstream activity, time spent on a page, time spent reviewingelements, links to data elements, and the like.

In embodiments, a data marketplace for a data collection system in anindustrial environment may comprise: an input system structured toreceive a plurality of data inputs relating to data sensed from or aboutone or more industrial machines; at least one of a data pool and a datastream to provide collected data within the marketplace; and aself-organization system for organizing at least one of the data inputsand the data pools based on a metric of success of the marketplace. Inembodiments, the self-organization system may optimize variations of theorganization of the data pool over time. The optimized variations may bebased on feedback to one or more measures of success. Theself-organization system may organize how data elements are presented inthe user interface of the marketplace. The self-organization system mayselect what data elements are presented, what data streams are obtainedas inputs to the marketplace, how data elements are described, whatmetadata is provided with data elements, a storage method for dataelements, a location within a communication network for the storageelements (such as in edge elements of a network), a data elementcombination method, and the like. A storage method may include a cacheor other “hot” storage method. A storage method may include slower, butless expensive storage locations. The data element combination methodmay be a data fusion method, a data multiplexing method, and the like.The self-organization system may receive feedback data, such as wherefeedback data includes success metrics and measures. Success metrics andmeasures may include profit measures, include yield measures, ratings,indicators of interest, and the like. Ratings include ratings may beprovided by users, purchasers, by licensees, reviewers. Success metricsand measures may include indicators of interest. Indicators of interestmay include clickstream activity, time spent on a page activity, timespent reviewing elements, time spent reviewing elements, links to dataelements, and the like. The self-organization system may determine thevalue of data streams. The value of data streams may determine whichdata streams are offered for sale by the data marketplace. The ratingsmay include user ratings. The ratings may include purchaser ratings,licensee ratings, reviewer ratings, and the like.

In embodiments, a data marketplace for a data collection system in anindustrial environment may comprise: an input interface structured toreceive data inputs from or about one or more of a plurality ofindustrial machines; at least one of a data pool and a data stream toprovide collected data within the marketplace; and a rights managementengine for managing permissions to access the data in the marketplace.In embodiments, at least one parameter of the rights management enginemay be automatically configured by a machine learning facility based ona metric of success of the marketplace. The rights management engine mayassign rights to participants of the data marketplace. The rights mayinclude business strategy and solution rights, liaison rights, marketingrights, security rights, technology rights, testbed rights, and thelike. The metrics and measures of success may include profit measures,yield measures, ratings, and the like. The ratings may include userratings, purchaser ratings, include licensee ratings, reviewer ratings,and the like. The metrics and measures success may include indicators ofinterest, such as where interest includes clickstream activity, timespent on a page, time spent reviewing elements, and links to dataelements.

In embodiments, a data marketplace for a data collection system in anindustrial environment may comprise: an input interface structured toreceive data inputs from or about one or more of a plurality ofindustrial machines; at least one of a data pool and a data stream toprovide collected data within the marketplace; and a data brokeringengine configured to execute a data transaction among at least twomarketplace participants. In embodiments, at least one parameter of thedata brokering engine may be automatically configured by a machinelearning facility based on a metric of success of the marketplace. Adata transaction input may include a marketplace value rating. Amarketplace value rating may be assigned to a marketplace participant. Amarketplace value rating may be assigned to a marketplace participant isassigned based on the value of input provided by the participant to themarketplace. A data transaction may be a trade transaction, a saletransaction, is a payment transaction, and the like. The metrics andmeasures of success may include profit measures, yield measures,ratings, and the like. The ratings may include user ratings. The ratingsmay include purchaser ratings, licensee ratings, reviewer ratings, andthe like. The metrics and measures success may include indicators ofinterest. The indicators of interest may include clickstream activity,time spent on a page, include time spent reviewing elements, links todata elements, and the like.

In embodiments, a data marketplace for a data collection system in anindustrial environment may comprise: an input interface structured toreceive data inputs from or about one or more of a plurality ofindustrial machines; at least one of a data pool and a data stream toprovide collected data within the marketplace; and a pricing engine forsetting a price for at least one data element within the marketplace. Inembodiments, pricing may be automatically configured for the pricingengine by a machine learning facility based on a metric of success ofthe marketplace. The metrics and measures of success may include profitmeasures, yield measures, include ratings, and the like. The ratings mayinclude user ratings. The ratings may include purchaser ratings,licensee ratings, reviewer ratings, and the like. The metrics andmeasures success may include indicators of interest. The indicators ofinterest may include clickstream activity, time spent on a page, includetime spent reviewing elements, links to data elements, and the like.

In embodiments, a data marketplace for a data collection system in anindustrial environment may comprise: an input interface structured toreceive data inputs from or about one or more of a plurality ofindustrial machines; at least one of a data pool and a data stream toprovide collected data within the marketplace; and a user interface forpresenting a data element and at least one mechanism by which a partyusing the marketplace can obtain access to the at least one data streamor data pool. In embodiments, pricing may be automatically configuredfor the pricing engine by a machine learning facility based on a metricof success of the marketplace. The metrics and measures of success mayinclude profit measures, yield measures, include ratings, and the like.The ratings may include user ratings. The ratings may include purchaserratings, licensee ratings, reviewer ratings, and the like. The metricsand measures success may include indicators of interest. The indicatorsof interest may include clickstream activity, time spent on a page,include time spent reviewing elements, links to data elements, and thelike.

In embodiments, a data collection system in an industrial environmentmay comprise: a policy automation system for a data collection system inan industrial environment, comprising: a plurality of rules selectedamong roles, permissions and uses, the plurality of rulescommunicatively coupled to policies, protocols, and policy inputs; aplurality of policies selected among compliance, fault, configuration,accounting, provisioning, and security policies for defining how devicesare created, deployed and managed, the plurality of policiescommunicatively coupled to policies, protocols and policy inputs and apolicy input interface structured to receive policy inputs used as aninput to at least one of a rule, policy and protocol definition.

In embodiments, a data marketplace may comprise: an input interfacestructured to receive marketplace inputs; a plurality of data pools tostore collected data, including marketplace inputs and make collecteddata available for use by the marketplace; and data streams that includedata from data pools.

As described herein and in Appendix B attached hereto, intelligentindustrial equipment and systems may be configured in various networks,including self-forming networks, private networks, Internet-basednetworks, and the like. One or more of the smart heating systems asdescribed in Appendix B that may incorporate hydrogen production,storage, and use may be configured as nodes in such a network. Inembodiments, a smart heating system may be configured with one or morenetwork ports, such as a wireless network port that facilitateconnection through Wi-Fi and other wired and/or wireless communicationprotocols as described. The smart heating system includes a smarthydrogen production system and a smart hydrogen storage system, and thelike described in Appendix B and may be configured individually or as anintegral system connected as one or more nodes in a network ofindustrial equipment and systems. By way of this example, a smartheating system may be disposed in an on-site industrial equipmentoperations center, such as a portable trailer equipped withcommunication capabilities and the like. Such deployed smart heatingsystem may be configured, manually, automatically, or semi-automaticallyto join a network of devices, such as industrial data collection,control, and monitoring nodes and participate in network management,communication, data collection, data monitoring, control, and the like.

In another example of a smart heating system participating in a networkof industrial equipment monitoring, control, and data collection devicesin that a plurality of the smart heating systems may be configured intoa smart heating system sub-network. In embodiments, data generated bythe sub-network of devices may be communicated over the network ofindustrial equipment using the methods and systems described herein.

In embodiments, the smart heating system may participate in a network ofindustrial equipment as described herein. By way of this example, one ormore of the smart heating systems, as depicted in FIG. 182, may beconfigured as an IoT device, such as IoT device 13500 and the likedescribed herein. In embodiments, the smart heating system 13502 maycommunicate through an access point, over a mobile ad hoc network ormechanism for connectivity described herein for devices and systemselements and/or through network elements described herein.

In embodiments, one or more smart heating systems described in AppendixB may incorporate, integrate, use, or connect with facilities,platforms, modules, and the like that may enable the smart heatingsystem to perform functions such as analytics, self-organizing storage,data collection and the like that may improve data collection, deployincreased intelligence, and the like. Various data analysis techniques,such as machine pattern recognition of data, collection, generation,storage, and communication of fusion data from analog industrialsensors, multi-sensor data collection and multiplexing, self-organizingdata pools, self-organizing swarm of industrial data collectors, andothers described herein may be embodied in, enabled by, used incombination with, and derived from data collected by one or more of thesmart heating systems.

In embodiments, a smart heating system may be configured with local datacollection capabilities for obtaining long blocks of data (i.e., longduration of data acquisition), such as from a plurality of sensors, at asingle relatively high-sampling rate as opposed to multiple sets of datataken at different sampling rates. By way of this example, the localdata collection capabilities may include planning data acquisitionroutes based on historical templates and the like. In embodiments, thelocal data collection capabilities may include managing data collectionbands, such as bands that define a specific frequency band and at leastone of a group of spectral peaks, true-peak level, crest factor and thelike.

In embodiments, one or more smart heating systems may participate as aself-organizing swarm of IoT devices that may facilitate industrial datacollection. The smart heating systems may organize with other smartheating systems, IoT devices, industrial data collectors, and the liketo organize among themselves to optimize data collection based on thecapabilities and conditions of the smart heating system and needs tosense, record, and acquire information from and around the smart heatingsystems. In embodiments, one or more smart heating systems may beconfigured with processing intelligence and capabilities that mayfacilitate coordinating with other members, devices, or the like of theswarm. In embodiments, a smart heating system member of the swarm maytrack information about what other smart heating systems in a swarm arehandling and collecting to facilitate allocating data collectionactivities, data storage, data processing and data publishing among theswarm members.

In embodiments, a plurality of smart heating systems may be configuredwith distinct burners but may share a common hydrogen production systemand/or a common hydrogen storage system. In embodiments, the pluralityof smart heating systems may coordinate data collection associated withthe common hydrogen production and/or storage systems so that datacollection is not unnecessarily duplicated by multiple smart heatingsystems. In embodiments, a smart heating system that may be consuminghydrogen may perform the hydrogen production and/or storage datacollection so that as smart heating system may prepare to consumehydrogen, they coordinate with other smart heating systems to ensurethat their consumption is tracked, even if another smart heating systemperforms the data collection, handling, and the like. In embodiments,smart heating systems in a swarm may communicate among each other todetermine which smart heating system will perform hydrogen consumptiondata collection and processing when each smart heating system preparesto stop consumption of hydrogen, such as when heating, cooking, or otheruse of the heat is nearing completion and the like. By way of thisexample when a plurality of smart heating systems is actively consuminghydrogen, data collection may be performed by a first smart heatingsystem, data analytics may be performed by a second smart heatingsystem, and data and data analytics recording or reporting may beperformed by a third smart heating system. By allocating certain datacollection, processing, storage, and reporting functions to differentsmart heating systems, certain smart heating systems with sufficientstorage, processing bandwidth, communication bandwidth, available energysupply and the like may be allocated an appropriate role. When a smartheating system is nearing an end of its heating time, cooking time, orthe like, it may signal to the swarm that it will be going into powerconservation mode soon and, therefore, it may not be allocated toperform data analysis or the like that would need to be interrupted bythe power conservation mode.

In embodiments, another benefit of using a swarm of smart heatingsystems as disclosed herein is that data storage capabilities of theswarm may be utilized to store more information than could be stored ona single smart heating system by sharing the role of storing data forthe swarm.

In embodiments, the self-organizing swarm of smart heating systemsincludes one of the systems being designated as a master swarmparticipant that may facilitate decision making regarding the allocationof resources of the individual smart heating systems in the swarm fordata collection, processing, storage, reporting and the like activities.

In embodiments, the methods and systems of self-organizing swarm ofindustrial data collectors may include a plurality of additionalfunctions, capabilities, features, operating modes, and the likedescribed herein. In embodiments, a smart heating system may beconfigured to perform any or all of these additional features,capabilities, functions, and the like without limitation.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “another,” as used herein, is defined as at least a secondor more. The terms “including” and/or “having,” as used herein, aredefined as comprising (i.e., open transition).

While only a few embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that manychanges and modifications may be made thereunto without departing fromthe spirit and scope of the present disclosure as described in thefollowing claims. All patent applications and patents, both foreign anddomestic, and all other publications referenced herein are incorporatedherein in their entireties to the full extent permitted by law.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions, and thelike. The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor, or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor, and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions, and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions, and programs as described hereinand elsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions, orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server, and the like. Theserver may include one or more of memories, processors, computerreadable transitory and/or non-transitory media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other servers, clients, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the server. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationswithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, code,and/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient, and other variants such as secondary client, host client,distributed client, and the like. The client may include one or more ofmemories, processors, computer readable transitory and/or non-transitorymedia, storage media, ports (physical and virtual), communicationdevices, and interfaces capable of accessing other clients, servers,machines, and devices through a wired or a wireless medium, and thelike. The methods, programs, or codes as described herein and elsewheremay be executed by the client. In addition, other devices required forexecution of methods as described in this application may be consideredas a part of the infrastructure associated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of a program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code, and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

Various embodiments described in this document relate to communicationprotocols that improve aspects of communication between nodes on a datanetwork. These aspects include, for instance, average, worst case, orvariability in communication delay, channel utilization, and/or errorrate. These embodiments are primarily described in the context of packetswitched networks, and more particularly in the context of InternetProtocol (IP) based packet switched networks. However, it should beunderstood that at least some of the embodiments are more generallyapplicable to data communication that does not use packet switching orIP, for instance based on circuit-switched of other forms of datanetworks.

Furthermore, various embodiments are described in the context of databeing sent from a “server” to a “client.” It should be understood thatthese terms are used very broadly, roughly analogous to “data source”and “data destination.” Furthermore, in at least some applications ofthe techniques, the nodes are peers, and may alternate roles as “server”and “client” or may have both roles (i.e., as data source and datadestination) concurrently. However, for the sake of exposition, exampleswhere there is a predominant direction of data flow from a “server” nodeto a “client” node are described with the understanding that thetechniques described in these examples are applicable to many othersituations.

One example for a client-server application involves a server passingmultimedia (e.g., video and audio) data, either recorded or live, to aclient for presentation to a user. Improved aspects of communicationfrom the client to the server in such an example can reducedcommunication delay, for instance providing faster startup, reducedinstances of interrupted playback, reduced instances of bandwidthreduction, and/or increased quality by more efficient channelutilization (e.g., by avoiding use of link capacity in retransmissionsor unnecessary forward error correction). This example is useful forexposition of a number of embodiments. However, it must be recognizedthat this is merely one of many possible uses of the approacheddescribed below.

FIG. 183 shows a high-level block diagram of some components that may beinterconnected on a portion of a data network. A general example of acommunication connection or session arranged on today's Internet may berepresented as a client node 125 (e.g., a client computer) communicatingwith a server node 111 (e.g., a server computer) over one network or aninterconnection of multiple networks 151-152. For example, the clientand server nodes may communicate over the public Internet using theInternet Protocol (IP). FIG. 183 additionally shows a number of nodes161, 162 positioned on the respective networks 151, 152, and a clientproxy 123 on one of the networks 152.

Referring to FIG. 184, in an example involving conventionalcommunication techniques, the client node 125 hosts a client application222, which communicates with a TCP module 226 that implements a TCP. TheTCP module 226 communicates with an IP module 228 that implements anInternet Protocol for communicating between nodes on the interconnectionof networks. The communication passes between nodes of the networks overa channel 230 (i.e., an abstraction of the path comprising physicallinks between equipment interconnecting the nodes of the network).Similarly, the server node 111 hosts a server application 212, a TCPmodule 216, and an IP module 218. When the server application 111 andthe client application 222 communicate, for example, with data beingpassed from the server application to the client application, TCP module216 at the server node 111 and the TCP layer 226 at the client node 125interact to implement the two endpoints for the TCP.

Generally, data units 201 (e.g., encoding of multimedia frames or otherunits of application data) generated by the server application 212 arepassed to the TCP module 216. The TCP module assembles data payloads202, for example, concatenating multiple data units 201 and/or bydividing data units 201 into multiple data payloads 202. In thediscussion below, these payloads are referred to in some instances asthe “original” or “uncoded” “packets” or original or uncoded “payloads,”which are communicated to the client (i.e., destination) node in thenetwork. Therefore, it should be understood that the word “packet” isnot used with any connotation other than being a unit of communication.In the TCP embodiment illustrated in FIG. 184, each data payload 202 is“wrapped” in a TCP packet 204, which is passed to the IP module 218,which further wraps the TCP packet 204 in an IP packet 206 fortransmission from the server node 111 to the client node 125, over whatis considered to be an IP layer channel 230 linking the server node 111and the client node 125. Note that at lower layers, such as at a datalink layer, further wrapping, unwrapping, and/or rewrapping of the IPpacket 206 may occur, however, such aspects are not illustrated in FIG.184. Generally, each payload 202 is sent in at least one TCP packet 204and a corresponding IP packet 206, and if not successfully received bythe TCP module 226 at the client node 125, may be retransmitted again bythe TCP module 216 at the server node 111 to result in successfuldelivery. The data payloads 202 are broken down into the data units 201originally provided by the server application 212 and are then deliveredin the same order to the client application 222 as they were provided bythe server application 212.

TCP implements a variety of features, including retransmission of lostpackets, maintaining order of packets, and congestion control to avoidcongestion at nodes or links along the path through the network and toprovide fair allocation of the limited bandwidth between and within thenetworks at intermediate nodes. For example, TCP implements a “windowprotocol” in which only a limited number (or range of sequence numbers)of packets are permitted to be transmitted for which end-to-endacknowledgments have not yet been received. Some implementations of TCPadjust the size of the window, for example, starting initially with asmall window (“slow start”) to avoid causing congestion. Someimplementations of TCP also control a rate of transmission of packets,for example, according to the round-trip-time and the size of thewindow.

The description below details one or more alternatives to conventionalTCP-based communication as illustrated in FIG. 184. In general, thesealternatives improve one or more performance characteristics, forexamples, one or more of overall throughput, delay, and jitter. In someapplications, these performance characteristics are directly related toapplication level performance characteristics, such as image quality ina multimedia presentation application. Referring to FIG. 183, in anumber of examples, these alternatives are directed to improvingcommunication between the server node 111 and at least one client node125. One example of such communication is streaming media from theserver node 111 to the client nodes 125, however, it should berecognized that this is only one of many examples where the describedalternatives can be used.

It should also be understood that the network configuration illustratedin FIG. 183 is merely representative of a variety of configurations. Anumber of these configurations may have paths with disparatecharacteristics. For example, a path from the server node 111 to theclient node 125 may pass over links using different types of equipmentand with very different capacities, delays, error rates, degrees ofcongestion etc. In many instances, it is this disparity that presentschallenges to achieving end-to-end communication that achieves highrate, low delay and/or low jitter. As one example, the client node 125may be a personal communication device on a wireless cellular network,the network 152 in FIG. 183 may be a cellular carrier's private wirednetwork, and network 151 may be the public Internet. In another example,the client node 125 may be a “WiFi” node of a private WLAN, network 152may be a private LAN, and network 151 may be the public Internet.

A number of the alternatives to conventional TCP make use of a PacketCoding (PC) approach. Furthermore, a number of these approaches make useof PC essentially at the Transport Layer. Although different embodimentsmay have different features, these implementations are genericallyreferred to below as Packet Coding Transmission Control Protocol(PC-TCP). Other embodiments are also described in which the same orsimilar PC approaches are used at other layers, for instance, at a datalink layer (e.g., referred to as PC-DL), and therefore it should beunderstood that in general features described in the context ofembodiments of PC-TCP may also be incorporated in PC-DL embodiments.

Before discussing particular features of PC-TCP in detail, a number ofembodiments of overall system architectures are described. The laterdescription of various embodiments of PC-TCP should be understood to beapplicable to any of these system architectures, and others.

Architectures and Applications Transport Layer Architectures KernelImplementation

Referring to FIG. 185, in one architecture, the TCP modules at theserver node 111 and the client node 125 are replaced with PC-TCP modules316 and 326, respectively. Very generally, the PC-TCP module 316 at theserver accepts data units 201 from the server application 212 and formsoriginal data payloads 202 (i.e., “uncoded packets”, formed internallyto the PC-TCP module 316 and not illustrated). Very generally, thesedata payloads 202 are transported to and/or reconstructed at the PC-TCPmodule 326 at the client node 125, where the data units 201 areextracted and delivered to the client application 222 in the same orderas provided by the server application 212. As described in substantiallymore detail below, at least some embodiments of the PC-TCP modules makeuse of Random Linear Coding (RLC) for forming packets 304 fortransmission from the source PC-TCP module to the destination PC-TCPmodule, with each packet 304 carrying a payload 302, which for at leastsome of the packets 304 is formed from a combination of multipleoriginal payloads 202. In particular, at least some of the payloads 202are formed as linear combinations (e.g., with randomly generatedcoefficients in a finite field) of original payloads 202 to implementForward Error Correction (FEC), or as part of a retransmission or repairapproach in which sufficient information is not provided using FEC toovercome loss of the packets 304 on the channel 230. Furthermore, thePC-TCP modules 316 and 326 together implement congestion control and/orrate control to generally coexist in a “fair” manner with othertransport protocols, notably conventional TCP.

One software implementation of the PC-TCP modules 316 or 326, issoftware modules that are integrated into the operating system (e.g.,into the “kernel”, for instance, of a Unix-based operating system) inmuch the same manner that a conventional TCP module is integrated intothe operating system. Alternative software implementations are discussedbelow.

Referring to FIG. 186, in an example in which the client node 125 is asmartphone on a cellular network (e.g., on an LTE network) and theserver node 111 is accessible using IP from the client node, theapproach illustrated in FIG. 185 is used with one end-to-end PC-TCPsession linking the client node 125 and the server node 111. The IPpackets 300 carrying the packets 304 of the PC-TCP session traverse thechannel between the nodes using conventional approaches withoutrequiring any non-conventional handling between the nodes at theendpoints of the session.

Alternative Software Implementations

The description above includes modules generically labeled “PC-TCP.” Inthe description below, a number of different implementations of thesemodules are presented. It should be understood that, in general, anyinstance of a PC-TCP module may be implemented using any of thedescribed or other approaches.

Referring to FIG. 187, in some embodiments, the PC-TCP module 326 (orany other instance of PC-TCP module discussed in this document) isimplemented as a PC-TCP module 526, which includes a Packet Coding (PC)module 525 that is coupled to (i.e., communicates with) a convention UDPmodule 524. Essentially each PC-TCP packet described above consists of aPC packet “wrapped” in a UDP packet. The UDP module 524 thencommunicates via the IP modules in a conventional manner. In someimplementations, the PC module 525 is implemented as a “user space”process, which communicates with a kernel space UDP module, while inother implementations, the PC module 525 is implement in kernel space.

Referring to FIG. 188, in some embodiments, the PC module 625, or itsfunction, is integrated into a client application 622, which thencommunicates directly with the conventional UDP module 524. The PC-TCPmodule 626 therefore effectively spans the client application 622 andthe kernel implementation of the UDP module 524. While use of UDP tolink the PC modules at the client and at the server has certainadvantages, other protocols may be used. One advantage of UDP is thatreliable transmission through use of retransmission is not part of theUDP protocol, and therefore error handling can be carried out by the PCmodules.

Referring to FIG. 189, in some implementations, a PC-TCP module 726 isdivided into one part, referred to as a PC-TCP “stub” 727, whichexecutes in the kernel space, and another part, referred to as thePC-TCP “code” 728, which executes in the user space of the operatingsystem environment. The stub 727 and the code 728 communicate to providethe functionality of the PC-TCP module.

It should be understood that these software implementations are notexhaustive. Furthermore, as discussed further below, in someimplementations, a PC-TCP module of any of the architectures or examplesdescribed in this document may be split among multiple hosts and/ornetwork nodes, for example, using a proxy architecture.

Proxy Architectures Conventional Proxy Node

Referring to FIG. 190, certain conventional communication architecturesmake use of proxy servers on the communication path between the clientnode 125 and the server node 111. For example, a proxy node 820 hosts aproxy server application 822. The client application 222 communicateswith the proxy server application 822, which acts as an intermediary incommunication with the server application 212 (not shown in FIG. 190).It should be understood that a variety of approaches to implementingsuch a proxy are known. In some implementations, the proxy applicationis inserted on the path without the client node necessarily being aware.In some implementations, a proxy client 812 is used at the client node,in some cases forming a software “shim” between the application layerand the transport layer of the software executing at the client node,with the proxy client 812 passing communication to the proxy serverapplication. In a number of proxy approaches, the client application 222is aware that the proxy is used, and the proxy explicitly acts as anintermediary in the communication with the server application. Aparticular example of such an approach makes use of the SOCKS protocol,in which the SOCKS proxy client application (i.e., an example of theproxy client 812) communicates with a SOCKS proxy server application(i.e., an example of the proxy server application 822). The client andserver may communicate over TCP/IP (e.g., via TCP and IP modules 826 band 828 b, which may be implemented together in one TCP module), and theSOCKS proxy server application fulfills communication requests (i.e.,with the server application) on behalf of the client application (e.g.,via TCP and IP modules 826 a and 828 a). Note that the proxy serverapplication may also perform functions other than forwardingcommunication, for example, providing a cache of data that can be usedto fulfill requests from the client application.

First Alternative Proxy Node

Referring to FIG. 191, in an alternative proxy architecture, a proxynode 920 hosts a proxy server application 922, which is similar to theproxy server application 822 of FIG. 190. The client application 222communicates with the proxy server application 922, for example asillustrated using conventional TCP/IP, and in some embodiments using aproxy client 812 (e.g., as SOCKS proxy client), executing at the clientnode 125. As illustrated in FIG. 191, the proxy server application 922communicates with a server application using a PC-TCP module 926, whichis essentially the same as the PC-TCP module 326 shown in FIG. 185 forcommunicating with the PC-TCP module 316 at the server node 111.

In some embodiments, the communication architecture of FIG. 191 and theconventional communication architecture of FIG. 184 may coexist in thecommunication between the client application and the server applicationmay use PC-TCP, conventional TCP, or concurrently use both PC-TCP andTCP. The communication approach may be based on a configuration of theclient application and/or based on dialog between the client and serverapplications in establishing communication between them.

Referring to FIG. 192, in an example of the architecture shown in FIG.191, the proxy application 922 is hosted in a gateway 1020 that links aLAN 1050 to the Internet. A number of conventional client nodes 125 a-zare on the LAN, and make use of the proxy server application tocommunicate with one or more server applications over the Internet.Various forms of the gateway 1020 may be used, for instance, a router,firewall, modem (e.g., cable modem, DSL modem etc.). In such examples,the gateway 1020 may be configured to pass conventional TCP/IPcommunication between the client nodes 125 a-z and the Internet, and forcertain server applications or under certain conditions (e.g.,determined by the client, the server, or the gateway) use the proxy tomake use of PC-TCP for communication over the Internet.

It should be understood that the proxy architecture shown in FIG. 191may be equally applied to server nodes 111 that communicate with a proxynode using TCP/IP, with the proxy providing PC-TCP communication withclient nodes, either directly or via client side proxies. In such cases,the proxy server application serving the server nodes may be hosted, forinstance, in a gateway device, such as a load balancer (e.g., as mightbe used with a server “farm”) that links the servers to the Internet. Itshould also be understood that in some applications, there is a proxynode associated with the server node as well as another proxy associatedwith the client node.

Integrated Proxy

Referring to FIG. 193, in some examples, a proxy server application1123, which provides essentially the same functionality as the proxyserver application 922 of FIG. 191, is resident on a client node 1121rather than being hosted on a separate network node as illustrated inFIG. 191. In such an example, the connection between the clientapplication 222 and the proxy server application 1123 is local, with thecommunication between them not passing over a data network (althoughinternally it may be passed via the IP 1129 software “stack”). Forexample, a proxy client 812 (e.g., a SOCKS client) interacts locallywith the proxy server application 1123, or the functions of the proxyclient 812 and the proxy server application 1123 are integrated into asingle software component.

Second Alternative Proxy Node

In examples of the first alternative proxy node approach introducedabove, communication between the client node and the proxy node usesconventional techniques (e.g., TCP/IP), while communication between theproxy node and the server node (or its proxy) uses PC-TCP 1127. Such anapproach may mitigate congestion and/or packet error or loss on the linkbetween the server node and the proxy node, however, it would notgenerally mitigate issues that arise on the link between the proxy nodeand the client node. For example, the client node and the proxy node maybe linked by a wireless channel (e.g., WiFi, cellular, etc.), which mayintroduce a greater degree of errors than the link between the serverand the proxy node over a wired network.

Referring to FIG. 194, in a second proxy approach, the client node 125hosts the PC-TCP module 326, or hosts or uses any of the alternatives ofsuch a module described in this document. The client application 222makes use of the PC-TCP module 326 at the client node to communicationwith a proxy node 1220. The proxy node essentially translates betweenthe PC-TCP communication with the client node 125 and conventional(e.g., TCP) communication with the server node. The proxy node 1220includes a proxy server application 1222, which makes use of a PC-TCPmodule 1226 to communicate with the client node (i.e., forms transportlayer link with the PC-TCP module 326) at the client node, and uses aconventional TCP module 826 a to communicate with the server.

Examples of such a proxy approach are illustrated in FIGS. 195-197.Referring to FIG. 195, an example of the proxy node 1220 is integratedin a wireless access device 1320 (e.g., a WiFi access point, router,etc.). The wireless access device 1320 is coupled to the server via awired interface 1351 and coupled to a wireless client node 125 via awireless interface 1352 at the access device and a wireless interface1353 at the client node. The wireless access device 1320 includes aproxy and communication stack implementation 1321, which includes themodules illustrated for the proxy 1220 in FIG. 194, and the wirelessclient node 125 includes an application and communication stackimplementation 1322, which includes the modules illustrated for theclient node 125 in FIG. 194. Note that the IP packets 300 passingbetween the wireless access device 1320 and the client node 125 aregenerally further “wrapped” using a data layer protocol, for example, indata layer packets 1350. As introduced above, in some implementations,rather than implementing the Packet Coding at the transport layer, in amodification of the approach shown in FIG. 195, the Packet Codingapproaches are implemented at the data link layer.

Referring to FIG. 196, the proxy node 1220 is integrated in a node of aprivate land network of a cellular service provider. In this example,communication between a server 111 and the proxy node 1220 useconventional techniques (e.g., TCP) over the public Internet, whilecommunication between the proxy node and the client node use PC-TCP. Itshould be understood that the proxy node 1220 can be hosted at variouspoints in the service provider's network, including without limitationat a gateway or edge device that connects the provider's private networkto the Internet (e.g., a Packet Data Network Gateway of an LTE network),and/or at an internal node of the network (e.g., a serving gateway, basestation controller, etc.). Referring to FIG. 197, a similar approach maybe used with a cable television based network. PC-TCP communication maypass between a head end device and a distribution network (e.g., afiber, coaxial, or hybrid fiber-coaxial network) to individual homes.For example, each home may have devices that include PC-TCP capabilitiesthemselves, or in some example, a proxy node (e.g., a proxy nodeintegrated in a gateway 1020 as shown in FIG. 192) terminates the PC-TCPconnections at each home. The proxy node that communicates with theserver 111 using conventional approaches, while communicating usingPC-TCP over the distribution network is hosted in a node in the serviceprovider's private network, for instance at a “head end” device 1220 bof the distribution network, or in a gateway device 1220 a that linksthe service provider's network with the public Internet.

Intermediate Proxy

Referring to FIG. 198, in another architecture, the channel between aserver node and a client node is broken in to independent tandem PC-TCPlinks. An intermediate node 1620 has two instances of a PC-TCP module1626 and 1627. One PC-TCP module 1626 terminates a PC-TCP channel andcommunicates with a corresponding PC-TCP module at the server (e.g.,hosted at the server node or at a proxy associated with the servernode). The other PC-TCP module 1627 terminates a PC-TCP channel andcommunicates with a corresponding PC-TCP module at the client (e.g.,hosted at the client node or at a proxy associated with the clientnode). The two PC-TCP modules 1626 and 1627 are coupled via a routingapplication 1622, which passes decoded data units provided by one of thePC-TCP modules (e.g., module 1626 from the server node) and to anotherPC-TCP module for transmission to the client.

Note that parameters of the two PC-TCP channels that are bridged at theintermediate node 1620 do not have to be the same. For example, thebridged channels may differ in their forward error correction code rate,block size, congestion window size, pacing rate, etc. In cases in whicha retransmission protocol is used to address packet errors or lossesthat are not correctable with forward error correction coding, thePC-TCP modules at the intermediate node request or service suchretransmission requests.

In FIG. 198, only two PC-TCP modules are shown, but it should beunderstood that the intermediate node 1620 may concurrently provide alink between different pairs of server and client nodes.

Referring to FIG. 199, an example of this architecture may involve theserver node 111 communicating with the intermediate node 1620, forexample, hosted in a gateway device 1720 of a service provider networkwith the intermediate node 1620 also communicating with the client node125 via a second PC-TCP link.

Recoding Node

Referring to FIG. 200, another architecture is similar to the one shownin FIG. 198 in that an intermediate node 1820 is on a path between theserver node 111 and the client node 125, with PC-TCP communicationpassing between it and the server node and between it and the clientnode.

In FIG. 198, the PC-TCP modules 1626, 1627 fully decode and encode thedata passing through the node. In the approach illustrated in FIG. 200,such complete decoding is not necessary. Rather, a recoding PC-TCPmodule 1822 receives payloads 1802 a-b from PC-TCP packets 1804 a-b, andwithout decoding to reproduce the original uncoded payloads 202 (notshown), the module uses the received PC-TCP packets to send PC-TCPpackets 304, with the coded payloads 302, toward the destination.Details of various recoding approaches are described further later inthis document. However, in general, the processing by the recodingPC-TCP module includes one or more of the following functions:forwarding PC-TCP packets without modification to the destination;“dropping” received PC-TCP packets without forwarding, for example, ifthe redundancy provided by the received packets are not needed on theoutbound link; generating and transmitting new PC-TCP packets to provideredundancy on the outbound link. Note that the recording PC-TCP modulemay also provide acknowledgement information on the inbound PC-TCP link(e.g., without requiring acknowledgement from the destination node), forexample, to the server, and process received acknowledgements on theoutbound link. The processing of the received acknowledgements mayinclude causing transmission of additional redundant information in thecase that the originally provided redundancy information was notsufficient for reconstruction of the payload data.

In general, the recoding PC-TCP module maintains separate communicationcharacteristics on the inbound and outbound PC-TCP channels. Therefore,although it does not decode the payload data, it does provide controland, in general, the PC-TCP channels may differ in their forward errorcorrection code rate, block size, congestion window size, pacing rate,etc.

Multipath Transmission Single Endpoint Pair

In examples described above, a single path links the server node 111 andthe client node 125. The possibility of using conventional TCPconcurrently with PC-TCP between two nodes was introduced. Moregenerally, communication between a pair of PC-TCP modules (i.e., one atthe server node 111 and one at the client node 125) may follow differentpaths. Internet protocol itself supports packets passing from one nodeto another following different paths and possibly being delivered out oforder. Multiple data paths or channels can link a pair of PC-TCP modulesand be used for a single session. Beyond native multi-path capabilitiesof IP networks, PC-TCP modules may use multiple explicit paths for aparticular session. For example, without intending to be exhaustive,combinations of the following types of paths may be used: Uncoded TCPand PC over UDP; PC over conventional TCP and UDP; and PC-TCP overwireless LAN (e.g., WiFi, 802.11) and cellular data (e.g., 3G, LTE) orPC-TCP concurrently over multiple wireless base stations (e.g., viamultiple wireless LAN access points).

In some examples, Network Coding is used such that the multiple pathsfrom a server node to a client node pass through one or moreintermediate nodes at which the data is recoded, thereby causinginformation for different data units to effectively traverse differentpaths through the network.

One motivation for multipath connection between a pair of endpointsaddresses possible preferential treatment of TCP traffic rather than UDPtraffic. Some networks (e.g., certain public Wi-Fi, cable televisionnetworks, etc.) may limit the rate of UDP traffic, or drop UDP packetspreferentially compared to TCP (e.g., in the case of congestion). It maybe desirable to be able to detect such scenarios efficiently withoutlosing performance. In some embodiments, a PC-TCP session initiallyestablishes and divides the transmitted data across both a TCP and a UDPconnection. This allows comparison of the throughput achieved by bothconnections while sending distinct useful data on each connection. Anidentifier is included in the initial TCP and UDP handshake packets toidentify the two connections as belonging to the same coded PC-TCPsession, and non-blocking connection establishment can be employed so asto allow both connections to be opened at the outset without additionaldelay. The transmitted data is divided across the two connections usinge.g., round-robin (sending alternating packets or runs of packets oneach connection) or load-balancing/back pressure scheduling (sendingeach packet to the connection with the shorter outgoing data queue).Such alternation or load balancing can be employed in conjunction withtechniques for dealing with packet reordering. Pacing rate andcongestion window size can be controller separately for the UDP and theTCP connection, or can be controlled together. By controlling the twoconnections together (e.g., using only a single congestion window toregulate the sum of the number of packets in flight on both the TCP andUDP connections) may provide a greater degree of “fairness” as comparedto separate control.

In some examples, the adjustment of the fraction of messages transmittedover each data path/protocol is determined according to the relativeperformance/throughput of the data paths/protocols. In some examples,the adjustment of allocation of messages occurs only during an initialportion of the transmission. In other examples, the adjustment ofallocation of messages occurs on an ongoing basis throughout thetransmission. In some examples, the adjustment reverses direction (e.g.,when a data path stops preferentially dropping UDP messages, the numberof messages transmitted over that data path may increase).

In some embodiments the PC-TCP maintains both the UDP based traffic andthe TCP based traffic for the duration of the session. In otherembodiments, the PC-TCP module compares the behavior of the UCP and TCPtraffic, for example over a period specified in terms of time intervalor number of packets, where these quantities specifying the period canbe set as configuration parameters and/or modified based on previouscoded TCP sessions, e.g., the comparison period can be reduced oreliminated if information on relative TCP/UDP performance is availablefrom recent PC-TCP sessions. If the UDP connection achieves betterthroughput, the PC-TCP session can shift to using UDP only. If the TCPconnection achieves better throughput, the PC-TCP session can shift tousing TCP. In some embodiments, different types of traffic are sent overthe TCP link rather than the UDP link. In one such example, the UDPconnection is used to send some forward error correction for packetswhere it is beneficial to reduce retransmission delays, e.g., the lastblock of a file or intermediate blocks of a stream. In this example, theuncoded packets may be sent over a TCP stream with forward errorcorrection packets sent over UDP. If the receiver can use the forwarderror correction packets to recover from erasures in the TCP stream, amodified implementation of the TCP component of the receiver's PC-TCPmodule may be able to avoid using a TCP-based error recovery procedure.On the other hand, non-delivery of a forward error correction packetdoes not cause an erasure of the data that is to be recovered at thereceiver, and therefore unless there is an erasure both on the UDP pathand on the TCP path, dropping of a UDP packet does not cause delay.

Distributed Source

In some examples, multiple server nodes communicate with a client node.One way this can be implemented is with there being multiplecommunication sessions each involving one server node and one clientnode. In such an implementation, there is little or no interactionbetween a communication session between one server node and the clientnode and another communication session between another server node andthe client node. In some examples, each server node may have differentparts of a multimedia file, with each server providing its parts forcombination at the client node.

Distributed Content Delivery

In some examples, there is some relationship between the contentprovided by different servers to the client. One example of such arelationship is use of a distributed RAID approach in which redundancyinformation (e.g., parity information) for data units at one or moreservers is stored at and provided from another server. In this way,should a data unit not reach the client node from one of the servernodes, the redundancy information may be preemptively sent or requestedfrom the other node, and the missing data unit reconstructed.

In some examples, random linear coding is performed on data units beforethey are distributed to multiple server nodes as an alternative to useof distributed RAID. Then each server node establishes a separatecommunication session with the client node for delivery of part of thecoded information. In some of these examples, the server nodes havecontent that has already been at least partially encoded and thencached, thereby avoiding the necessity of repeating that partialencoding for different client nodes that will receive the sameapplication data units. In some examples, the server nodes may implementsome of the functionality of the PC modules for execution duringcommunication sessions with client nodes, for example, having theability to encode further redundancy information in response toacknowledgment information (i.e., negative acknowledgement information)received from a client node.

In some implementations, the multiple server nodes are content deliverynodes to which content is distributed using any of a variety of knowntechniques. In other implementations, these multiple server nodes areintermediary nodes at which content from previous content deliverysessions was cached and therefore available without requiringre-delivery of the content from the ultimate server node.

In some examples of distributed content delivery, each server to clientconnection is substantially independent, for example, with independentlydetermined communication parameters (e.g., error correction parameters,congestion window size, pacing rate, etc.). In other examples, at leastsome of the parameters are related, for example, with characteristicsdetermined on one server-to-client connection being used to determinehow the client node communicates with other server nodes. For example,packet arrival rate, loss rate, and differences in one-way transmissionrate, may be measured on one connections and these parameters may beused in optimizing multipath delivery of data involving other servernodes. One manner of optimization may involve load balancing acrossmultiple server nodes or over communication links on the paths from theserver nodes to the client nodes.

In some implementations, content delivery from distributed server nodesmaking use of PC-TCP, either using independent sessions or usingcoordination between sessions, may achieve the performance ofconventional distributed content delivery but requiring a smaller numberof server nodes. This advantage may arise due to PC-TCP providing lowerlatency and/or lower loss rates than achieved with conventional TCP.

Multicast

FIGS. 201-202 show two examples of delivery of common content tomultiple destination nodes simultaneously via multicast connections. Theadvantage of multicast is that a single packet or block of N packets hasto be sent by the source node into the network and the network willattempt to deliver the packets to all destination nodes in the multicastgroup. If the content needs to be delivered reliably, then TCP will mostlikely be used as the transport layer protocol. To achieve reliability,TCP requires destination nodes to respond with acknowledgments andspecify the packets that each destination node is missing. If there aretens of thousands or hundreds of thousands of receivers, and eachdestination node is missing a different packet or set of packets, thenumber of different retransmissions to the various receivers willundercut the advantages of the simultaneous transmission of the contentto all destination nodes at once. With network coding and forward errorcorrection, a block of N packets can be sent to a large number ofmulticast destination nodes at the same time. The paths to thesemultiple destination nodes can be similar (all over a large WiFi orEthernet local area network) or disparate (some over WiFi, some overcellular, some over fiber links, and some over various types ofsatellite networks). The algorithms described above that embodytransmission and congestion control, forward error correction, senderbased pacing, receiver based pacing, stream based parameter tuning,detection and correction for missing and out of order packets, use ofinformation across multiple connections, fast connection start and stop,TCP/UDP fallback, cascaded coding, recoding by intermediate nodes, andcoding of the ACKs can be employed to improve the throughput andreliability of delivery to each of the multicast destination node. Whenlosses are detected and coding is used, the extra coded packets can besent to some or all destination nodes. As long as N packets are receivedat each destination node, the missing packets at each destination nodecan be reconstructed from the coded packets if the number of extra codedpackets match or exceed the number of packets lost at all of thereceivers. If fewer than N packets are received at any of thedestination nodes, any set of different coded packets from the block ofN packets can be retransmitted and used to reconstruct any missingpacket in the block at each of the destination nodes. If somedestination nodes are missing more than one packet, then the maximumnumber of coded packets to be retransmitted will be equal to the largestnumber of packets that are missing by any of the destination nodes.These few different coded packets can be used to reconstruct the missingpackets at each of the destination nodes. For example, if the mostpackets missing at any destination node is four, then any four differentcoded packets can be retransmitted.

Further Illustrative Examples

FIGS. 203-213 show exemplary embodiments of data communication systemsand devices and highlight various ways to implement the novel PC-TCPdescribed herein. These configurations identify some of the possiblenetwork devices, configurations, and applications that may benefit fromusing PC-TCP, but there are many more devices, configurations andapplications that may also benefit from PC-TCP. The followingembodiments are described by way of example, not limitation.

In an exemplary embodiment depicted in FIG. 203, a user device 404 suchas a smartphone, a tablet, a computer, a television, a display, anappliance, a vehicle, a home server, a gaming console, a streaming mediabox and the like, may include a PC-TCP proxy that may interface withapplications running in the user device 404. The application on the userdevice 404 may communicate with a resource in the cloud 402 a such as aserver 408. The server 408 may be a file server, a web server, a videoserver, a content server, an application server, a collaboration server,an FTP server, a list server, a telnet server, a mail server, a proxyserver, a database server, a game server, a sound server, a printserver, an open source server, a virtual server, an edge server, astorage device and the like, and may include a PC-TCP proxy that mayinterface with applications and/or processes running on the server 408.In embodiments, the server in the cloud may terminate the PC-TCPconnection and interface with an application on the server 408 and/ormay forward the data on to another electronic device in the network. Inembodiments, the data connection may travel a path that utilizes theresources on a number of networks 402 a, 402 b. In embodiments PC-TCPmay be configured to support multipath communication such as for examplefrom the video server 408 through a peering point 406, though a carriernetwork 402 b, to a wireless router or an access point 410 to the userdevice 404 and from the video server 408 through the peering point 406,though a carrier network 402 b, to a cellular base station or celltransmitter 412 to the user device 404. In embodiments, the PC-TCP mayinclude adjustable parameters that may be adjusted to improve multipathperformance. In some instances, the exemplary embodiment shown in FIG.203 may be referred to as an over-the-top (OTT) embodiment.

In embodiments, such as the exemplary embodiments shown in FIG. 204 andFIG. 205, other devices in the network may comprise PC-TCP proxies. Forexample, the wireless access point or router 410 and the base station orcell transmitter 412 may comprise PC-TCP proxies. In embodiments, theuser device 404 may also comprise a PC-TCP proxy (FIG. 205) or it maynot (FIG. 204). If the user device does not comprise a PC-TCP proxy, itmay communicate with the access point 410 and/or base station 412 usinga wireless or cellular protocol and/or conventional TCP or UDP protocol.The PC-TCP proxy in either or both the access point 410 and base station412 may receive data packets using these conventional communications andmay convert these communications to the PC-TCP for a connection to thevideo server 408. In embodiments, if conventional TCP provides thehighest speed connection between the end user device 404 and/or theaccess point 410 or the base station 412, then the PC-TCP proxy mayutilize only some or all of the features in PC-TCP that may be compliantwith and may compliment conventional TCP implementations and transmitthe data using the TCP layer.

FIG. 206 shows an exemplary embodiment where a user device may comprisea PC-TCP proxy and may communicate with the PC-TCP proxy server 408 onan internet. In this embodiment, an entity may provide support for highspeed internet connections by renting, buying services from, ordeploying at least one server in the network and allowing other serversor end user devices to communicate with it using PC-TCP. The at leastone server in the network running PC-TCP may connect to other resourcesin the network and/or end users using TCP or UDP.

In embodiments, such as the exemplary embodiments shown in FIG. 207 andFIG. 208, other devices in the network may comprise PC-TCP proxies. Forexample, the wireless access point or router 410 and the base station orcell transmitter 412 may comprise PC-TCP proxies. In embodiments, theuser device 404 may also comprise a PC-TCP proxy (FIG. 208) or it maynot (FIG. 207). If the user device does not comprise a PC-TCP proxy, itmay communicate with the access point 410 and/or base station 412 usinga wireless or cellular protocol and/or conventional TCP or UDP protocol.The PC-TCP proxy in either or both the access point 410 and base station412 may receive data packets using these conventional communications andmay convert these communications to the PC-TCP for a connection to thePC-TCP server 408. In embodiments, if conventional TCP provides thehighest speed connection between the end user device 404 and/or theaccess point 410 or the base station 412, then the PC-TCP proxy mayutilize only some or all of the features in PC-TCP that may be compliantwith and may compliment conventional TCP implementations and transmitthe data using the TCP layer.

In embodiments, at least some network servers 408 may comprise PC-TCPproxies and may communicate with any PC-TCP servers or devices usingPC-TCP. In other embodiments, network servers may communicate withPC-TCP servers or devices using conventional TCP and/or other transportprotocols running over UDP.

In exemplary embodiments as depicted in FIG. 209, ISPs and/or carriersmay host content on one or more servers that comprise PC-TCP proxies. Inembodiments, devices such as set-top boxes, cable boxes, digital videorecorders (DVRs), modems, televisions, smart televisions, internettelevisions, displays, and the like may comprise PC-TCP proxies. Theuser device 404 such as described above, may include a PC-TCP proxy thatmay interface with applications running in the user device 404. Theapplication on the user device 404 may communicate with a resource inthe cloud 402 c such as the server 408. The server 408 may be any typeof communications server as describe above, and may include a PC-TCPproxy that may interface with applications and/or processes running onthe server 408. In embodiments, the server in the cloud may terminatethe PC-TCP connection and interface with an application on the server408 and/or may forward the data on to another electronic device in thenetwork. In embodiments, the data connection may travel a path thatutilizes the resources on a number of networks 402 a, 402 b, 402 c. Inembodiments PC-TCP may be configured to support multipath communicationsuch as for example from the video server 408 through the direct peeringpoint (DP) 406, to a wireless router or an access point 410 or a basestation 412 to the user device 404 and from the video server 408directly to the access point 410 and/or to a cellular base station orcell transmitter 412 to the user device 404. In embodiments, the PC-TCPmay include adjustable parameters that may be adjusted to improvemultipath performance.

The exemplary placements of networking devices in the communicationscenarios described above should not be taken as limitations. It shouldbe recognized that PC-TCP proxies can be placed in any network deviceand may support any type of data connection. That is, any type ofend-user device, switching device, routing device, storage device,processing device and the like, may comprise PC-TCP proxies. Also,PC-TCP proxies may reside only in the end-nodes of a communication pathand/or only at two nodes along a connection path. However, PC-TCPproxies may also reside in more than two nodes of a communication pathand may support multi-cast communications and multipath communications.PC-TCP proxies may be utilized in point-to-point communication networks,multi-hop networks, meshed networks, broadcast networks, storagenetworks, and the like.

Packet Coding (PC)

The description above focuses on architectures in which a packet codingapproach is deployed, and in particular architectures in which atransport layer PC-TCP approach is used. In the description below, anumber of features of PC-TCP are described. It should be understood thatin general, unless otherwise indicated, these features are compatiblewith one another and can be combined in various combinations to addressparticular applications and situations.

Data Characteristics

As introduced above, data units (e.g., audio and/or video frames) aregenerally used to form data packets, for example, with one data unit perdata packet, with multiple data units per data packet, or in someinstances separating individual data units into multiple data packets.In some applications, the data units and associated data frames form astream (e.g., a substantially continuous sequence made available overtime without necessarily having groupings or boundaries in thesequence), while in other applications, the data units and associateddata frames form one or more batches (e.g., a grouping of data that isrequired as a whole by the recipient).

In general, stream data is generated over time at a source and consumedat a destination, typically at a substantially steady rate. An exampleof a stream is a multimedia stream associated with person-to-personcommunication (e.g., a multimedia conference). Delay (also referred toas latency) and variability in delay (also referred to as jitter) areimportant characteristics of the communication of data units from asource to a destination.

An extreme example of a batch is delivery of an entire group of data,for example, a multiple gigabyte sized file. In some such examples,reducing the overall time to complete delivery (e.g., by maximizingthroughput) of the batch is of primary importance. One example of batchdelivery that may have very sensitive time (and real-time update)restraints is database replication.

In some applications, the data forms a series of batches that requiredelivery from a source to a destination. Although delay in start ofdelivery and/or completion of delivery of a batch of data units may beimportant, in many applications overall throughput may be mostimportant. An example of batch delivery includes delivery of portions ofmultimedia content, for instance, with each batch corresponding tosections of viewing time (e.g., 2 seconds of viewing time or 2 MB perbatch), with content being delivered in batches to the destination wherethe data units in the batches are buffered and used to construct acontinuous presentation of the content. As a result, an importantconsideration is the delivery of the batches in a manner that providescontinuity between batches for presentation, without “starving” thedestination application because a required batch has not arrived intime. In practice, such starving may cause “freezing” of videopresentation in multimedia, which is a phenomenon that is all toofamiliar to today's users of online multimedia delivery. Anotherimportant consideration is reduction in the initial delay in providingthe data units of the first batch to the destination application. Suchdelay is manifested, for example, in a user having to wait for initialstartup of video presentation after selecting multimedia for onlinedelivery. Another consideration in some applications is overallthroughput. This may arise, for example, if the source application hascontrol over a data rate of the data units, for example, being able toprovide a higher fidelity version of the multimedia content if higherthroughput can be achieved. Therefore, an important consideration may beproviding a sufficiently high throughput in order to enable delivery ofa high fidelity version of the content (e.g., as opposed to greatlycompressed version or a backed-off rate of the content resulting inlower fidelity).

Various packet coding approaches described below, or selection ofconfiguration parameters of those approaches, address considerationsthat are particularly relevant to the nature of the characteristics ofthe data being transported. In some examples, different approaches orparameters are set in a single system based on a runtime determinationof the nature of the characteristics of the data being transported.

Channel Characteristics

In general, the communication paths that link PC-TCP source anddestination endpoints exhibit both relatively stationary or consistentchannel characteristics, as well as transient characteristics.Relatively stationary or consistent channel characteristics can include,for example, capacity (e.g., maximum usable throughput), latency (e.g.,transit time of packets from source to destination, variability intransit time), error rate (e.g., average packet erasure or error rate,burst characteristics of erasures/errors). In general, such relativelystationary or consistent characteristics may depend on the nature of thepath, and more particularly on one or more of the links on the path. Forexample, a path with a link passing over a 4G cellular channel mayexhibit very different characteristics than a path that passes over acable television channel and/or a WiFi link in a home. As discussedfurther below, at least some of the approaches to packet coding attemptto address channel characteristic differences between types ofcommunication paths. Furthermore, at least some of the approachesinclude aspects that track relatively slow variation in characteristics,for example, adapting to changes in average throughput, latency, etc.

Communication characteristics along a path may also exhibit substantialtransient characteristics. Conventional communication techniques includeaspects that address transient characteristics resulting from congestionalong a communication path. It is well known that as congestionincreases, for example at a node along a communication path, it isimportant that traffic is reduced at that node in order to avoid anunstable situation, for instance, with high packet loss resulting frombuffer overruns, which then further increases data rates due toretransmission approaches. One common approach to addressingcongestion-based transients uses an adaptive window size of “in flight”packets that have not yet been acknowledged by their destinations. Thesize of the window is adapted at each of the sources to avoidcongestion-based instability, for example, by significantly reducing thesize of the window upon detection of increased packet erasure rates.

In addressing communication over a variety of channels, it has beenobserved that transients in communication characteristics may not be duesolely to conventional congestion effects, and that conventionalcongestion avoidance approaches may not be optimal or even desirable.Some effects that may affect communication characteristics, and that maytherefore warrant adaptation of the manner in which data is transmittedcan include one or more of the follow: Effects resulting from cellhandoff in cellular systems, including interruptions in delivery ofpackets or substantial reordering of packets delivered after handoff;Effects resulting from “half-duplex” characteristics of certain wirelesschannels, for example, in WiFi channels in which return packets from adestination may be delayed until the wireless channel is acquired forupstream (i.e., portable device to access point) communication; Effectsof explicit data shaping devices, for example, intended to throttlecertain classes of communication, for instance, based on a serviceprovider's belief that that class of communication is malicious or isconsuming more than a fair share of resources.

Although transient effects, which may not be based solely on congestion,may be tolerated using conventional congestion avoidance techniques, oneor more of the approaches described below are particularly tailored tosuch classes of effects with the goal of maintaining efficient use of achannel without undue “over-reaction” upon detection of a transientsituation, while still avoiding causing congestion-based packet loss.

Inter-Packet Coding

In general, the coding approaches used in embodiments described in thisdocument make use of inter-packet coding in which redundancy informationis sent over the channel such that the redundancy information in onepacket is generally dependent on a set of other packets that have beenor will be sent over the channel. Typically, for a set of N packets ofinformation, a total of N+K packets are sent in a manner that erasure orany K of the packets allows reconstruction of the original N packets ofinformation. In general, a group of N information packets, or a group ofN+K packets including redundancy information (depending on context), isreferred to below as a “block” or a “coding block.” One example of sucha coding includes N information packets without further coding, and thenK redundancy packets, each of which depends on the N informationpackets. However it should be understood more than K of the packets(e.g., each of the N K packets) may in some embodiments depend on allthe N information packets.

Forward Error Correction and Repair Retransmission

Inter-packet coding in various embodiments described in this documentuse one or both of pre-emptive transmission of redundant packets,generally referred to as forward error correction (FEC), andtransmission of redundant packets upon an indication that packets haveor have a high probability of having been erased based on feedback,which is referred to below as repair and/or retransmission. The feedbackfor repair retransmission generally comes from the receiver, but moregenerally may come from a node or other channel element on the path tothe receiver, or some network element having information about thedelivery of packets along the path. In the FEC mode, K redundant packetsmay be transmitted in order to be tolerant of up to K erasures of the Npackets, while in the repair mode, in some examples, for each packetthat the transmitter believes has been or has high probability of havingbeen erased, a redundant packet it transmitted from the transmitter,such that if in a block of N packets, K packets are believed to havebeen erased based on feedback, the transmitter sends at least anadditional K packets.

As discussed more fully below, use of a forward error correction modeversus a repair mode represents a tradeoff between use of more channelcapacity for forward error correction (i.e., reduced throughout ofinformation) versus incurring greater latency in the presence oferasures for repair retransmission. As introduced above, the datacharacteristics being transmitted may determine the relative importanceof throughput versus latency, and the PC-TCP modules may be configuredor adapted accordingly.

If on average the packet erasure rate E is less than K/(N+K), then “onaverage” the N+K packets will experience erasure of K or fewer of thepackets and the remaining packets will be sufficient to reconstruct theoriginal N. Of course even if E is not greater than K/(N+K), randomvariability, non-stationarity of the pattern of erasures etc. results insome fraction of the sets of N K packets having greater than K erasures,so that there is insufficient information to reconstruct the N packetsat the destination. Therefore, even using FEC, at least some groups of Ninformation packets will not be reconstructable. Note, for example, withE=0.2, N=8, and K=2, even though only 2 erasures may be expected onaverage, the probability of more than 2 erasures is greater than 30%,and even with E=0.1 this probability is greater than 7%, therefore thenature (e.g., timing, triggering conditions etc.) of the retransmissionapproaches may be significant, as discussed further below. Also, asdiscussed below, the size of the set of packets that are coded togetheris significant. For example, increasing N by a factor of 10 to K+N=100reduces the probably of more than the average number of 20 erasures(i.e., too many erasures to reconstruct the N=80 data packets) from over7% to less than 0.1%.

Also, as discussed further below, there is a tradeoff between use oflarge blocks of packets (i.e., large N) versus smaller blocks. For aparticular code rate R=N/(N+K), longer blocks yield a higher probabilityof being able to fully recover the N information packets in the presenceof random errors. Accordingly, depending on the data characteristics,the PC-TCP modules may be configured to adapt to achieve a desiredtradeoff.

In general, in embodiments that guarantee delivery of the N packets,whether or not FEC is used, repair retransmission approaches are used toprovide further information for reconstructing the N packets. Ingeneral, in preferred embodiments, the redundancy information is formedin such a manner that upon an erasure of a packet, the redundancyinformation that is sent from the transmitter does not depend on thespecific packets that were erased, and is nevertheless suitable forrepairing the erasure independent of which packet was erased.

Random Linear Coding

In general, a preferred approach to inter-packet coding is based onRandom Linear Network Coding (RLNC) techniques. However, it should beunderstood that although based on this technology, not all features thatmay be associated with this term are necessarily incorporated. Inparticular, as described above in the absence of intermediate nodes thatperform recoding, there is not necessarily a “network” aspect to theapproach. Rather, redundancy information is generally formed bycombining the information packets into coded packets using arithmeticcombinations, and more specifically, as sums of products of coefficientsand representation of the information packets over arithmetic fields,such as finite fields (e.g., Galois Fields of order p^(n)). In general,the code coefficients are chosen from a sufficiently large finite fieldin a random or pseudo-random manner, or in another way that thecombinations of packets have a very low probability or frequency ofbeing linearly dependent. The code coefficients, or a compressed version(e.g., as a reference into a table shared by the transmitter andreceiver), are included in each transmitted combination of data units(or otherwise communicated to the receiver) and used for decoding at thereceiver. Very generally, the original information packets may berecovered at a receiver by inverting the arithmetic combinations. Forexample, a version of Gaussian Elimination may be used to reconstructthe original packets from the coded combinations. A key feature of thisapproach is that for a set of N information packets, as soon at thereceiver has at least N linearly independent combinations of thoseinformation packets in received packets, it can reconstruct the originaldata units. The term “degree of freedom” is generally used below torefer to a number of independent linear combinations, such that if Ndegrees of freedom have been specified for N original packets, then theN original packets can be reconstructed; while if fewer than N degreesof freedom are available, it may not be possible to fully reconstructany of the N original packets. If N+K linearly independent linearcombinations are sent, then any N received combinations (i.e., Nreceived degrees of freedom) are sufficient to reconstruct the originalinformation packets.

In some examples, the N K linearly independent combinations comprise Nselections of the N “uncoded” information packets (essentially N−1 zerocoefficients and one unit coefficient for each uncoded packet), and Kcoded packets comprising the random arithmetic combination with Nnon-zero coefficients for the N information packets. The N uncodedpackets are transmitted first, so that in the absence of erasures theyshould be completely received as soon as possible. In the case of oneerasure of the original N packets, the receiver must wait for thearrival of one redundant packet (in addition to the N−1 originalpackets), and once that packet has arrived, the erased packet may bereconstructed. In the case of forward error correction, the K redundantpackets follow (e.g., immediately after) the information packets, andthe delay incurred in reconstructing the erased information packetdepends on the transmission time of packets. In the case of repairretransmission, upon detection of an erasure or high probability of anerasure, the receiver provides feedback to the transmitter, which sendsthe redundancy information upon receiving the feedback. Therefore, thedelay in being able to reconstruct the erased packet depends on theround-trip-time from the receiver to the transmitter and back.

As discussed in more detail below, feedback from the receiver to thetransmitter may be in the form of acknowledgments sent from the receiverto the transmitter. This feedback in acknowledgements at least informsthe transmitter of a number of the N packets of a block that have beensuccessfully received (i.e., the number of received degrees of freedom),and may provide further information that depends on the specific packetsthat have been received at the receiver although such furtherinformation is not essential.

As introduced above, packets that include the combinations of originalpackets generally also include information needed to determine thecoefficients used to combine the original packets, and informationneeded to identify which original packets were used in the combination(unless this set, such as all the packets of a block, is implicit). Insome implementations, the coefficients are explicitly represented in thecoded packets. In some embodiments, the coefficients are encoded withreference to shared information at the transmitter and the receiver. Forinstance, tables of pre-generated (e.g., random, pseudo random, orotherwise selected) coefficients, or sets of coefficients, may be storedand references into those tables are used to determine the values of thecoefficients. The size of such a table determines the number of paritypackets that can be generated while maintaining the linear independenceof the sets of coefficients. It should be understood that yet other waysmay be used to determine the coefficients.

Another feature of random linear codes is that packets formed as linearcombinations of data units may themselves be additively combined toyield combined linear combinations of data units. This process isreferred to in some instances as “recoding,” as distinct from decodingand then repeating encoding.

There are alternatives to the use of RLNC, which do not necessarilyachieve similar optimal (or provably optimum, or near optimal)throughput as RLNC, but that give excellent performance in somescenarios when implemented as described herein. For example, variousforms of parity check codes can be used. Therefore, it should beunderstood that RLNC, or any particular aspect of RLNC, is not anessential feature of all embodiments described in this document.

Batch Transmission

As introduced above, in at least some applications, data to betransmitted from a transmitter to a receiver forms a batch (i.e., asopposed to a continuous stream), with an example of a batch being a fileor a segment (e.g., a two second segment of multimedia) of a file.

In an embodiment of the PC-TCP modules, the batch is transferred fromthe transmitter to the receiver as a series of blocks, with each blockbeing formed from a series of information packets. In general, eachblock has the same number of information packets, however use of samesize blocks is not essential.

The transmitter PC-TCP module generally receives the data units from thesource application and forms the information packets of the successiveblocks of the batch. These information packets are queued at thetransmitter and transmitted on the channel to the receiver. In general,at the transmitter, the dequeuing and transmission of packets to thereceiver makes use of congestion control and/or rate control mechanismsdescribed in more detail below. The transmitter PC-TCP also retains theinformation packets (or sufficient equivalent information) to constructredundancy information for the blocks. For instance, the transmitterPC-TCP buffers the information packets for each block for which thereremains the possibility of an unrecovered erasure of a packet duringtransit from the transmitter to the receiver.

In general, the receiver provides feedback to the transmitter. Variousapproaches to determining when to provide the feedback and whatinformation to provide with the feedback are described further below.The feedback provides the transmitter with sufficient information todetermine that a block has been successfully received and/orreconstructed at the receiver. When such success feedback for a blockhas been received, the transmitter no longer needs to retain theinformation packets for the block because there is no longer thepossibility that redundancy information for the block will need to besent to the receiver.

The feedback from the receiver to the transmitter may also indicate thata packet is missing. Although in some cases the indication that a packetis missing is a premature indication of an erasure, in this embodimentthe transmitter uses this missing feedback to trigger sending redundantinformation for a block. In some examples, the packets for a block arenumbered in sequence of transmission, and the feedback represents thehighest number received and the number of packets (i.e., the number ofdegrees of freedom) received (or equivalently the number of missingpackets or remaining degrees of freedom needed) for the block. Thetransmitter addresses missing packet feedback for a block through thetransmission of redundant repair blocks, which may be used by thereceiver to reconstruct the missing packets and/or original packets ofthe block.

As introduced above, for each block, the transmitter maintainssufficient information to determine the highest index of a packetreceived at the receiver, the number of missing packets transmittedprior to that packet, and the number of original or redundancy packetsafter the highest index received that have been transmitted (i.e., are“in flight” unless erased in transit) or queued for transmission at thetransmitter.

When the transmitter receives missing packet feedback for a block, ifthe number of packets for the block that are “in flight” or queue wouldnot be sufficient if received successfully (or are not expected to be inview of the erasure rate), the transmitter computes (or retrievesprecomputed) a new redundant packet for the block and queues it fortransmission. Such redundancy packets are referred to as repair packets.In order to reduce the delay in reconstructing a block of packets at thereceiver, the repair packets are sent preferentially to the informationpackets for later blocks. For instance, the repair packets are queued ina separate higher-priority queue that is used to ensure transmission ofrepair packets preferentially to the queue of information packets.

In some situations, feedback from the receiver may have indicated that apacket is missing. However, that packet may later arrive out of order,and therefore a redundant packet for that block that was earliercomputed and queued for transmission is no longer required to bedelivered to the receiver. If that redundant packet has not yet beentransmitted (i.e., it is still queued), that packet may be removed fromthe queue thereby avoiding wasted use of channel capacity for a packetthat will not serve to pass new information to the receiver.

In the approach described above, redundancy packets are sent as repairpackets in response to feedback from the receiver. In some examples,some redundancy packets are sent pre-emptively (i.e., as forward errorcorrection) in order to address possible packet erasures. One approachto send such forward error correction packets for each block. However,if feedback has already been received at the transmitter that asufficient number of original and/or coded packets for a block have beenreceived, then there is no need to send further redundant packets forthe block.

In an implementation of this approach, the original packets for all theblocks of the batch are sent first, while repair packets are beingpreferentially sent based on feedback from the receiver. After all theoriginal packets have been transmitted, and the queue of repair packetsis empty, the transmitter computes (or retrieves precomputed) redundancypackets for blocks for which the transmitter has not yet receivedfeedback that the blocks have been successfully received, and queuesthose blocks as forward error correction packets for transmission in thefirst queue. In general, because the repair blocks are sent with higherpriority than the original packets, the blocks for which successfeedback has not yet been received are the later blocks in the batch(e.g., a trailing sequence of blocks of the batch).

In various versions of this approach, the number and order oftransmission of the forward error correction packets are determined invarious ways. A first way uses the erasure rate to determine how manyredundant packets to transmit. One approach is to send at least oneredundant packet for each outstanding block. Another approach is to senda number of redundancy packets for each outstanding block so that basedon an expectation of the erasure rate of the packets that are queued andin flight for the block will yield a sufficient number of successfullyreceived packets in order to reconstruct the block. For example, if afurther n packets are needed to reconstruct a block (e.g., a number n<Npackets of the original N packets with N−n packets having been erased),then n+k packets are sent, for instance, with n+k≥n/E, where E is anestimate of the erasure rate on the channel.

Another way of determining the number and order of forward errorcorrection packets addresses the situation in which a block transmissiontime is substantially less than the round-trip-time for the channel.Therefore, the earliest of the blocks for which the transmitter has notreceived success feedback may in fact have the success feedback inflight from the receiver to the transmitter, and therefore sendingforward error correction packets may be wasteful. Similarly, even iffeedback indicating missing packet feedback for a block is receivedsufficiently early, the transmitter may still send a repair packetwithout incurring more delay in complete reconstruction of the entirebatch than would be achieved by forward error correction.

In an example, the number of forward error correction packets queued foreach block is greater for later blocks in the batch than for earlierones. A motivation for this can be understood by considering the lastblock of the batch where it should be evident that it is desirable tosend a sufficient number of forward error correction packets to ensurehigh probability of the receiver having sufficient information toreconstruct the block without the need from transmission of a repairpacket and the associated increase in latency. On the other hand, it ispreferable to send fewer forward error correction packets for theprevious (or earlier) block because in the face of missing packetfeedback from the receiver, the transmitter may be able to send a repairpacket before forward error correction packets for all the later blockshave been sent, thereby not incurring a delay in overall delivery of thebatch.

In one implementation, after all the original packets have been sent,and the transmitter is in the forward error correction phase in which itcomputes and sends the forward error correction packets, if thetransmitter receives a missing packet feedback from the receiver, itcomputes and sends a repair packet for the block in question (ifnecessary) as described above, and clears the entire queue of forwarderror correction packets. After the repair packet queue is again empty,the transmitter again computes and queues forward error correctionpackets for the blocks for which it has not yet received successfeedback. In an alternative somewhat equivalent implementation, ratherthan clearing the forward error correction queue upon receipt of amissing packet feedback, the transmitter removes forward errorcorrection packets from the queue as they are no longer needed based onfeedback from the receiver. In some examples, if success feedback isreceived for a block for which there are queued forward error correctionpackets, those forward error correction packets are removed from thequeue. In some examples, the feedback from the receiver may indicatethat some but not all of the forward error correction packets in thequeue are no longer needed, for example, because out-of-order packetswere received but at least some of the original packets are stillmissing.

An example of the way the transmitter determines how many forward errorcorrection packets to send is that the transmitter performs acomputation:

(N+g(i)−a _(i))/(1−p)−f _(i)

where

P=smoothed loss rate,

N=block size,

i=block index defined as number of blocks from last block,

a_(i)=number of packets acked from block i,

f_(i)=packets in-flight from block i, and

g(i)=a decreasing function of i,

to determine the number of FEC packets for a block.

In some examples, g(i) is determined as a maximum of a configurableparameter, m and N−i. In some examples, g(i) is determined as N−p(i)where p is a polynomial, with integer rounding as needed

It should be understood that in some alternative implementations, atleast some forward error correction packets may be interspersed with theoriginal packets. For example, if the erasure rate for the channel isrelatively high, then at least some number of redundancy packets may beneeded with relatively high probability for each block, and there is anoverall advantage to preemptively sending redundant FEC packets as soonas possible, in addition to providing the mechanism for feedback basedrepair that is described above.

It should be also understood that use of subdivision of a batch intoblocks is not necessarily required in order to achieve the goal ofminimizing the time to complete reconstruction of the block at thereceiver. However, if the forward error correction is applied uniformlyto all the packets of the batch, then the preferential protection oflater packets would be absent, and therefore, latency caused by erasureof later packets may be greater than using the approach described above.However, alternative approaches to non-uniform forward error protection(i.e., introduction of forward error correction redundancy packets) maybe used. For example, in the block based approach described above,packets of the later blocks each contribute to a greater number offorward error correction packets than do earlier ones, and analternative approach to achieving this characteristic maybe to use anon-block based criterion to construction of the redundancy packets inthe forward error correction phase. However, the block based approachdescribed above has advantages of relative simplicity and generalrobustness, and therefore even if marginally “suboptimal” provides anoverall advantageous technical solution to minimizing the time tocomplete reconstruction within the constraint of throughput and erasureon the channel linking the transmitter and receiver.

Another advantage of using a block-based approach is that, for example,when a block within the batch, say the m^(th) block of M blocks of thebatch has an erasure, the repair packet that is sent from thetransmitter depends only on the N original packets of the m^(th) block.Therefore, as soon as the repair packet arrives, and the available(i.e., not erased) N−1 packets of the block arrive, the receiver has theinformation necessary to repair the block. Therefore, by constructingthe repair packet without contribution of packets in later blocks of thebatch, the latency of the reconstruction of the block is reduced.Furthermore, by having the repair packets depend on only N originalpackets, the computation required to reconstruct the packets of theblock is less than if the repair packets depend on more packets.

It should be understood that even in the block based transmission of abatch of packets, the blocks are not necessarily uniform in size, andare not necessarily disjoint. For example, blocks may overlap (e.g., by50%, 75%, etc.) thereby maintaining at least some of the advantages ofreduced complexity in reconstruction and reduced buffering requirementsas compared to treating the batch as one block. An advantage of suchoverlapping blocks may be a reduced latency in reconstruction becauserepair packets may be sent that do not require waiting for originalpackets at the receiver prior to reconstruction. Furthermore,non-uniform blocks may be beneficial, for example, to increase theeffectiveness of forward error correction for later block in a batch byusing longer blocks near the end of a batch as compared to near thebeginning of a batch.

In applications in which the entire batch is needed by the destinationapplication before use, low latency of reconstruction may be desirableto reduce buffering requirements in the PC-TCP module at the receiver(and at the transmitter). For example, all packets that may contributeto a later received repair packet are buffered for their potentialfuture use. In the block based approach, once a block is fullyreconstructed, then the PC-TCP module can deliver and discard thosepackets because they will not affect future packet reconstruction.

Although described as an approach to delivery of a batch of packets, theformation of these batches may be internal to the PC-TCP modules,whether or not such batches are formed at the software applicationlevel. For example, the PC-TCP module at the transmitter may receive theoriginal data units that are used to form the original packets via asoftware interface from the source application. The packets aresegmented into blocks of N packets as described above, and the packetsqueued for transmission. In one embodiment, as long as the sourceapplication provides data units sufficiently quickly to keep the queuefrom emptying (or from emptying for a threshold amount of time), thePC-TCP module stays in the first mode (i.e., prior to sending forwarderror correction packets) sending repair packets as needed based onfeedback information from the receiver. When there is a lull in thesource application providing data units, then the PC-TCP module declaresthat a batch has been completed, and enters the forward error correctionphase described above. In some examples, the batch formed by the PC-TCPmodule may in fact correspond to a batch of data units generated by thesource application as a result of a lull in the source applicationproviding data units to the PC-TCP module while it computes data unitsfor a next batch, thereby inherently synchronizing the batch processingby the source application and the PC-TCP modules.

In one such embodiment, the PC-TCP module remains in the forward errorcorrection mode for the declared batch until that entire batch has beensuccessfully reconstructed at the receiver. In another embodiment, ifthe source application begins providing new data units before thereceiver has provided feedback that the previous batch has beensuccessfully reconstructed, the transmitter PC-TCP module begins sendingoriginal packets for the next batch at a lower priority than repair orforward error correction packets for the previous batch. Such anembodiment may reduce the time to the beginning of transmission of thenext batch, and therefore reduces the time to successful delivery of thenext batch.

In the embodiments in which the source application does not necessarilyprovide the data in explicit batches, the receiver PC-TCP moduleprovides the data units in order to the destination application withoutnecessarily identifying the block or batch boundaries introduced at thetransmitter PC-TCP module. That is, in at least some implementations,the transmitter and receiver PC-TCP modules provide a reliable channelfor the application data units without exposing the block and batchstructure to the applications.

As described above for certain embodiments, the transmitter PC-TCPmodule reacts to missing packet feedback from the receiver PC-TCP moduleto send repair packets. Therefore, it should be evident that themechanism by which the receiver sends such feedback may affect theoverall behavior of the protocol. For example, in one example, thereceiver PC-TCP module sends a negative acknowledgment as soon as itobserves a missing packet. Such an approach may provide the lowestlatency for reconstruction of the block. However, as introduced above,missing packets may be the result of out-of-order delivery. Therefore, aless aggressive generation of missing packet feedback, for example, bydelay in transmission of a negative acknowledgment, may reduce thetransmission of unnecessary repair packets with only a minimal increasein latency in reconstruction of that block. However, such delay insending negative acknowledgements may have an overall positive impact onthe time to successfully reconstruct the entire block because laterblocks are not delayed by unnecessary repair packets. Alternativeapproaches to generation of acknowledgments are described below.

In some embodiments, at least some of the determination of when to sendrepair packets is performed at the transmitter PC-TCP. For example, thereceiver PC-TCP module may not delay the transmission of missing packetfeedback, and it is the transmitter PC-TCP module that delays thetransmission of a repair packet based on its weighing of the possibilityof the missing packet feedback being based on out-of-order delivery asopposed to erasure.

Protocol Parameters

Communication between two PC-TCP endpoints operates according toparameters, some of which are maintained in common by the endpoints, andsome of which are local to the sending and/or the receiving endpoint.Some of these parameters relate primarily to forward error correctionaspects of the operation. For example, such parameters include thedegree of redundancy that is introduced through the coding process. Asdiscussed below, further parameters related to such coding relate to theselection of packets for use in the combinations. A simple example ofsuch selection is segmentation of the sequence of input data units into“frames” that are then independently encoded. In addition to the numberof such packets for combination (e.g., frame length), other parametersmay relate to overlapping and/or interleaving of such frames of dataunits and/or linear combinations of such data units.

Further parameters relate generally to transport layer characteristicsof the communication approach. For example, some parameters relate tocongestion avoidance, for example, representing a size of a window ofunacknowledged packets, transmission rate, or other characteristicsrelated to the timing or number of packets sent from the sender to thereceiver of the PC-TCP communication.

As discussed further below, communication parameters (e.g., codingparameters, transport parameters) may be set in various ways. Forexample, parameters may be initialized upon establishing a sessionbetween two PC-TCP endpoints. Strategies for setting those parametersmay be based on various sources of information, for example, accordingto knowledge of the communication path linking the sender and receiver(e.g., according to a classification of path type, such as 3G wirelessversus cable modem), or experienced communication characteristics inother sessions (e.g., concurrent or prior sessions involving the samesender, receiver, communication links, intermediate nodes, etc.).Communication parameters may be adapted during the course of acommunication session, for example, in response to observedcommunication characteristics (e.g., congestion, packet loss, round-triptime, etc.)

Transmission Control

Some aspects of the PC-TCP approaches relate to control of transmissionof packets from a sender to a receiver. These aspects are generallyseparate from aspects of the approach that determine what is sent in thepackets, for example, to accomplish forward error correction,retransmission, or the order in which the packets are sent (e.g.,relative priority of forward error correction packets versionretransmission packets). Given a queue of packets that are ready fortransmission from the sender to the receiver, these transmission aspectsgenerally relate to flow and/or congestion control.

Congestion Control

Current variants of TCP, including binary increase congestion control(BIC) and cubic-TCP, have been proposed to address the inefficiencies ofclassical TCP in networks with high losses, large bandwidths and longround-trip times. BIC-TCP and CUBIC algorithms have been used because oftheir stability. After a backoff, BIC increases the congestion windowlinearly then logarithmically to the window size just before backoff(denoted by W_(max)) and subsequently increases the window in ananti-symmetric fashion exponentially then linearly. CUBIC increases thecongestion window following backoff according to a cubic function withinflection point at W_(max). These increase functions cause thecongestion window to grow slowly when it is close to W_(max), promotingstability. On the other hand, other variants such as HTCP and FAST TCPhave the advantage of being able to partially distinguish congestion andnon-congestion losses through the use of delay as a congestion signal.

An alternative congestion control approach is used in at least someembodiments. In some such embodiments, we identify a concave portion ofthe window increase function as W_(concave)(t)=W_(max)+c₁(t−k)³ and aconvex portion of the window increase function asW_(convex)(t)=W_(max)+c₂(t−k)³ where c₁ and c₂ are positive tunableparameters and

$k = \sqrt[3]{\left( {\left( {{W\; \_ \; \max} - W} \right)/c_{1}} \right)}$

and W is the window size just after backoff.

This alternative congestion control approach can be flexibly tuned fordifferent scenarios. For example, a larger value of c₁ causes thecongestion window to increase more rapidly up to W_(max) and a largevalue of c₂ causes the congestion window to increase more rapidly beyondW_(max).

Optionally, delay is used as an indicator to exit slow start and move tothe more conservative congestion avoidance phase, e.g., when a smoothedestimate of RTT exceeds a configured threshold relative to the minimumobserved RTT for the connection. We can also optionally combine theincrease function of CUBIC or other TCP variants with the delay-basedbackoff function of HTCP.

In some embodiments, backoff is smoothed by allowing a lower rate oftransmission until the number of packets in flight decreases to the newwindow size. For instance, a threshold, n, is set such that once npackets have been acknowledged following a backoff, then one packet isallowed to be sent for every two acknowledged packets, which is roughlyhalf of the previous sending rate. This is akin to a hybrid window andrate control scheme.

Transmission Rate Control. Pacing Control by Sender

In at least some embodiments, pacing is used to regulate and/or spreadout packet transmissions, making the transmission rate less bursty.While pacing can help to reduce packet loss from buffer overflows,previous implementations of pacing algorithms have not shown clearadvantages when comparing paced TCP implementations to non-paced TCPimplementations. However, in embodiments where the data packets arecoded packets as described above, the combination of packet coding andpacing may have advantages. For example, since one coded packet may beused to recover multiple possible lost packets, we can use coding tomore efficiently recover from any spread out packet losses that mayresult from pacing. In embodiments, the combination of packet coding andpacing may have advantages compared to uncoded TCP with selectiveacknowledgements (SACK).

Classical TCP implements end-to-end congestion control based onacknowledgments. Variants of TCP designed for high-bandwidth connectionsincrease the congestion window (and consequently the sending rate)quickly to probe for available bandwidth but this can result in burstsof packet losses when it overshoots, if there is insufficient bufferingin the network.

A number of variants of TCP use acknowledgment feedback to determineround-trip time and/or estimate available bandwidth, and they differ inthe mechanisms with which this information is used to control thecongestion window and/or sending rate. Different variants have scenariosin which they work better or worse than others.

In one general approach used in one or more embodiments, a communicationprotocol may use smoothed statistics of intervals betweenacknowledgments of transmitted packets (e.g., a smoothed “ack interval”)to guide a transmission of packets, for example, by controllingintervals (e.g., an average interval or equivalently an averagetransmission rate) between packet transmissions. Broadly, this guidingof transmission intervals is referred to herein as “pacing.”

In some examples, the pacing approach is used in conjunction with awindow-based congestion control algorithm. Generally, the congestionwindow controls the number of unacknowledged packets that can be sent,in some examples using window control approaches that are the same orsimilar to those used in known variants of the TCP. In embodiments, thewindow control approach is based on the novel congestion controlalgorithms described herein.

A general advantage of one or more aspects is to improve functioning ofa communication system, for instance, as measured by total throughput,or delay and/or variation in delay. These aspects address a technicalproblem of congestion, and with it packet loss, in a network by using“pacing” to reduce that congestion.

An advantage of this aspect is that the separate control of pacing canprevent packets in the congestion window from being transmitted toorapidly compared to the rate at which they are getting through to theother side. Without separate pacing control, at least some conventionalTCP approaches would permit bursts of overly rapid transmission ofpackets, which might result in packet loss at an intermediate node onthe communication path. These packet losses may be effectivelyinterpreted by the protocol as resulting from congestion, resulting inthe protocol reducing the window size. However, the window size may beappropriate, for example, for the available bandwidth and delay of thepath, and therefore reducing the window size may not be necessary. Onthe other hand, reducing the peak transmission rate can have the effectof avoiding packet loss, for example, by avoiding overflow ofintermediate buffers on the path.

Another advantage of at least some implementations is prevention oflarge bursts of packet losses under convex window increase functions forhigh-bandwidth scenarios, by providing an additional finer level ofcontrol over the transmission process.

At least some implementations of the approach can leverage theadvantages of existing high-bandwidth variants of TCP such as H-TCP andCUBIC, while preventing large bursts of packet losses under their convexwindow increase functions and providing a more precise level of control.For example, pacing control may be implemented to pace the rate ofproviding packets from the existing TCP procedure to the channel, withthe existing TCP procedure typically further or separately limiting thepresentation of packets to the communication channel based, forinstance, on its window-based congestion control procedure.

In practice, a particular example in which separating pacing from windowcontrol has been observed to significantly outperform conventional TCPon 4G LTE.

Referring to FIG. 214, in one example, a source application 1010 passesdata to a destination application 1090 over a communication channel1050. Communication from the source application 1010 passes to atransport layer 1020, which maintains a communication session with acorresponding transport layer 1080 linked to the destination application1090. In general, the transport layers may be implemented as softwarethat executes on the same computer as their corresponding applications,however, it should be recognized that, for instance through the use ofproxy approaches, the applications and the transport layer elements thatare shown may be split over separate coupled computers. In embodiments,when a proxy is running on a separate machine or device from theapplication, the application may use the transport layer on its machineto communicate with the proxy layer.

In FIG. 214, the transport layer 1020 at the source application includesa window control and retransmission element 1030. In someimplementations, this element implements a conventional TCP approach,for instance, implementing H-TCP or CUBIC approaches. In otherimplementations, this element implements the novel congestion controlalgorithms described herein. The transport layer 1080 at the destinationmay implement a corresponding element 1060, which may provideacknowledgements of packets to the window control and retransmissionelement 1030 at the source. In general, element 1030 may implement awindow-based congestion control approach based on acknowledgements thatare received at the destination, however it should be understood that noparticular approach to window control is essential, and in someimplementations, element 1030 can be substituted with another elementthat implements congestion control using approaches other than windowcontrol.

Functionally, one may consider two elements of the protocol as beingloss recovery and rate/congestion control. Loss recovery can beimplemented either using conventional retransmissions or using coding oras a combination of retransmission and coding. Rate/congestion controlmay aim to avoid overrunning the receiver and/or the available channelcapacity, and may be implemented using window control with or withoutpacing, or direct rate control.

The channel 1050 coupling the transport layers in general may includelower layer protocol software at the source and destination, and aseries of communication links coupling computers and other network nodeson a path from the source to the destination.

As compared to conventional approaches, as shown in FIG. 192, a ratecontrol element 1040 may be on the path between the window control andretransmission element 1030 and the channel 1050. This rate controlelement may monitor acknowledgements that are received from thedestination, and may pass them on to the window control andretransmission element 1030, generally without delay. The rate controlelement 1040 receives packets for transmission on the channel 1050 fromthe window control and retransmission element 1030, and either passesthem directly to the channel 1050, or buffers them to limit a rate oftransmission onto the channel. For example, the rate control element1040 may require a minimum interval between successive packets, or maycontrol an average rate over multiple packets.

In embodiments, the acks that are transmitted on a return channel, fromthe destination to the source, may also be paced, and may also utilizecoding to recover from erasures and bursty losses. In embodiments,packet coding and transmission control of the acks may be especiallyuseful if there is congestion on the return channel.

In one implementation, the rate control element 1040 may maintain anaverage (i.e., smoothed) inter-packet delivery interval, estimated basedon the acknowledgement intervals (accounting for the number of packetsacknowledged in each ack). In some implementations this averaging may becomputed as a decaying average of past sample inter-arrival times. Thiscan be refined by incorporating logic for discarding large sample valuesbased on the determination of whether they are likely to have resultedfrom a gap in the sending times or losses in the packet stream, and bysetting configurable upper and lower limits on the estimated intervalcommensurate with particular characteristics of different knownnetworks. The rate control element 1040 may then use this smoothedinter-acknowledgement time to set a minimum inter-transmission time, forexample, as a fraction of the inter-acknowledgement time. This fractioncan be increased with packet loss and with rate of increase of RTT(which may be indicators that the current sending rate may be too high),and decreased with rate of decrease of RTT under low loss, e.g., using acontrol algorithm such as proportional control whose parameters can beadjusted to tradeoff between stability and responsiveness to change.Upper and lower limits on this fraction can be made configurableparameters, say 0.2 and 0.95. Transmission packets are then limited tobe presented to the channel 1050 with inter-transmission times of atleast this set minimum. In other implementations, inter-transmissionintervals are controlled to maintain a smoothed average interval or ratebased on a smoothed inter-acknowledgement interval or rate.

In addition to the short timescale adjustments of the pacing intervalwith estimated delivery interval, packet loss rate and RTT describedabove, there can also be a longer timescale control loop that modulatesthe overall aggressiveness of the pacing algorithm based on a smoothedloss rate calculated over a longer timescale, with, a higher loss rateindicating that pacing may be too aggressive. The longer timescaleadjustment can be applied across short duration connections by havingthe client maintain state across successive connections and includeinitializing information in subsequent connection requests. This longertimescale control may be useful for improving adaptation to diversenetwork scenarios that change dynamically on different timescales.

Referring to FIG. 215, in some implementations, the communicationchannel 1050 spans multiple nodes 1161, 1162 in one or aninterconnection of communication networks 1151, 1152. In FIG. 193, thesource application 1010 is illustrated as co-resident with the transportlayer 1020 on a source computer 1111, and similarly, the transport layer1080 is illustrated as co-resident on a destination computer 1190 withthe destination application 1090.

It should be recognized that although the description above focuses on asingle direction of communication, in general, a bidirectionalimplementation would include a corresponding path from the destinationapplication to the source application. In some implementations, bothdirections include corresponding rate control elements 1040, while inother applications, only one direction (e.g., from the source to thedestination application) may implement the rate control. For example,introduction of the rate control element 1040 at a server, or anotherdevice or network node on the path between the source application andthe transport layer 1080 at the destination, may not requiremodification of the software at the destination.

Pacing by Receiver

As described above, the sender can use acks to estimate therate/interval with which packets are reaching the receiver, the lossrate and the rate of change of RTT, and adjust the pacing intervalaccordingly. However, this estimated information may be noisy if acksare lost or delayed. On the other hand, such information can beestimated more accurately at the receiver with OWTT in place of RTT. Bybasing the pacing interval on the rate of change of OWTT rather than itsactual value, the need for synchronized clocks on sender and receivermay be obviated. The pacing interval can be fed back to the sender byincluding it as an additional field in the acks. The choice as towhether the pacing calculations are done at the sender or the receiver,or done every n packets rather than upon every packet reception, mayalso be affected by considerations of sender/receiver CPU/load.

Error Control

Classical TCP performs poorly on networks with packet losses. Congestioncontrol can be combined with coding such that coded packets are sentboth for forward error correction (FEC) to provide protection against ananticipated level of packet loss, as well as for recovering from actuallosses indicated by feedback from the receiver.

While the simple combination of packet coding and congestion control hasbeen suggested previously, the prior art does not adequately account fordifferences between congestion-related losses, bursty and/or randompacket losses. Since congestion-related loss may occur as relativelyinfrequent bursts, it may be inefficient to protect against this type ofloss using FEC.

In at least some embodiments, the rates at which loss events occur areestimated. A loss event may be defined as either an isolated packet lossor a burst of consecutive packet losses. In some examples, the sourcePC-TCP may send FEC packets at the estimated rate of loss events, ratherthan the estimated rate of packet loss. This embodiment is an efficientway to reduce non-useful FEC packets, since it may not bedisproportionately affected by congestion-related loss.

In an exemplary embodiment, the code rate and/or packet transmissionrate of FEC can be made tunable in order to trade-off between the usefulthroughput seen at the application layer (also referred to as goodput)and recovery delay. For instance, the ratio of the FEC rate to theestimated rate of loss events can be made a tunable parameter that isset with a priori knowledge of the underlying communications paths ordynamically adjusted by making certain measurements of the underlyingcommunications paths.

In another exemplary embodiment, the rate at which loss bursts of up toa certain length occur may be estimated, and appropriate burst errorcorrecting codes for FEC, or codes that correct combinations of burstand isolated errors, may be used.

In another exemplary embodiment, the FEC for different blocks can beinterleaved to be more effective against bursty loss.

In other exemplary embodiments, data packets can be sent preferentiallyover FEC packets. For instance, FEC packets can be sent at a configuredrate or estimated loss rate when there are no data packets to be sent,and either not sent or sent at a reduced rate when there are datapackets to be sent. In one implementation, FEC packets are placed in aseparate queue which is cleared when there are data packets to be sent.

In other exemplary embodiments, the code rate/amount of FEC in eachblock and/or the FEC packet transmission rate can be made a tunablefunction of the block number and/or the number of packets in flightrelative to the number of unacknowledged degrees of freedom of theblock, in addition to the estimated loss rate. FEC packets for laterblocks can be sent preferentially over FEC for earlier blocks, so as tominimize recovery delay at the end of a connection, e.g., the number ofFEC packets sent from each block can be a tunable function of the numberof blocks from the latest block that has not been fully acknowledged.The sending interval between FEC packets can be an increasing functionof the number of packets in flight relative to the number ofunacknowledged degrees of freedom of the corresponding block, so as totrade-off between sending delay and probability of losing FEC packets inscenarios where packet loss probability increases with transmissionrate.

In other exemplary embodiments, a variable randomly chosen fraction ofthe coding coefficients of a coded packet can be set to 1 or 0 in orderto reduce encoding complexity without substantially affecting erasurecorrection performance. In a systematic code, introducing 0 coefficientsonly after one or more densely coded packets (i.e., no or few 0coefficients) may be important for erasure correction performance. Forinstance, an initial FEC packet in a block could have each coefficientset to 1 with probability 0.5 and to a uniformly random value from thecoding field with probability 0.5. Subsequent FEC packets in the blockcould have each coefficient set to 0 with probability 0.5 and touniformly random value with probability 0.5.

Packet Reordering

As introduced above, packets may be received out of order on somenetworks, for example, due to packets traversing multiple paths,parallel processing in some networking equipment, reconfiguration of apath (e.g., handoff in cellular networks). Generally, conventional TCPreacts to out of order packets by backing off the size of the congestionwindow. Such a backoff may unnecessarily hurt performance if there is nocongestion necessitating a backoff.

In some embodiments, in an approach to handling packet reordering thatdoes not result from congestions, a receiver observing a gap in thesequence numbers of its received packets may delay sending anacknowledgment for a limited time. When a packet is missing, thereceiver does not immediately know if the packet has been lost (erased),or merely reordered. The receiver delays sending an acknowledgement thatindicates the gap to see if the gap is filled by subsequent packetarrivals. In some examples, upon observing a gap, the receiver starts afirst timer for a configurable “reordering detection” time interval,e.g., 20 milliseconds. If a packet from the gap is subsequently receivedwithin this time interval, the receiver starts a second timer for aconfigurable “gap filling” time interval, e.g., 30 milliseconds. If thefirst timer or the second timer expire prior to the gap being filled, anacknowledgement that indicates the gap is sent to the source.

Upon receiving the acknowledgment that indicates the gap in receivedpackets the source, in at least some embodiments, the sender determineswhether a repair packet should be sent to compensate for the gap in thereceived packets, for example, if a sufficient number of FEC packetshave not already been sent.

In another aspect, a sender may store relevant congestion control stateinformation (including the congestion window) prior to backoff, and arecord of recent packet losses. If the sender receives an ack reportinga gap/loss and then subsequently one or more other acks reporting thatthe gap has been filled by out of order packet receptions, any backoffcaused by the earlier ack can be reverted by restoring the stored statefrom before backoff.

In another aspect, a sender observing a gap in the sequence numbers ofits received acks may delay congestion window backoff for a limitedtime. When an ack is missing, the sender does not immediately know if apacket has been lost or if the ack is merely reordered. The senderdelays backing off its congestion window to see if the gap is filled bysubsequent ack arrivals. In some examples, upon observing a gap, thesender starts a first timer for a configurable “reordering detection”time interval, e.g., 20 milliseconds. If an ack from the gap issubsequently received within this time interval, the sender starts asecond timer for a configurable “gap filling” time interval, e.g., 30milliseconds. If the first timer or the second timer expires prior tothe gap being filled, congestion window backoff occurs.

In some examples, instead of using time intervals, packet sequencenumbers are used. For example, sending of an ack can be delayed until apacket which is a specified number of sequence numbers ahead of thereference lost packet is received. Similarly, backing off can be delayeduntil an acknowledgment of a packet which is a specified number ofsequence numbers ahead of the reference lost packet is received. In someexamples, these approaches have the advantage of being able to take intoaccount subsequently received/acknowledged reordered packets by shiftingthe sequence number of the reference lost packet as holes in the packetsequence get filled.

These methods for correcting packet reordering may be especially usefulfor multipath versions of the protocol, where there may be a largeamount of reordering.

Acknowledgements Delayed Acknowledgements

In at least some implementations, conventional TCP sends oneacknowledgment for every two data packets received. Such delayed ackingreduces ack traffic compared to sending an acknowledgment for every datapacket. This reduction in ack traffic is particularly beneficial whenthere is contention on the return channel, such as in Wi-Fi networks,where both data and ack transmissions contend for the same channel.

It is possible to reduce ack traffic further by increasing the ackinterval to a value n>2, i.e., sending one acknowledgment for every ndata packets. However, reducing the frequency with which acks arereceived by the sender can cause delays in transmission (when thecongestion window is full) or backoff (if feedback on losses isdelayed), which can hurt performance.

In one aspect, the sender can determine whether, or to what extent,delayed acking should be allowed based in part on its remainingcongestion window (i.e., its congestion window minus the number ofunacknowledged packets in flight), and/or its remaining data to be sent.For example, delayed acking can be disallowed if there is any packetloss, or if the remaining congestion window is below some (possiblytunable) threshold. Alternatively, the ack interval can be reduced withthe remaining congestion window. As another example, delayed acking canbe allowed if the amount of remaining data to be sent is smaller thanthe remaining congestion window, but disallowed for the last remainingdata packet so that there is no delay in acknowledging the last datapacket. This information can be sent in the data packets as a flagindicating whether delayed acking is allowed, or for example, as aninteger indicating the allowed ack interval.

Using relevant state information at the sender to influence delayedacking may allow an increase in the ack interval beyond the conventionalvalue of 2, while mitigating the drawbacks described above that a largerack interval across the board might have.

To additionally limit the ack delay, each time an ack is sent, a delayedack timer can be set to expire with a configured delay, say 25milliseconds. Upon expiration of the timer, any data packets receivedsince the last ack may be acknowledged, even if fewer packets than theack interval n have arrived. If no packets have been received since thelast ack, an ack may be sent upon receipt of the next data packet.

Parameter Control. Initialization

In some embodiments, to establish session parameters for the PC-TCPmodules are set to a predefine set of default parameters. In otherembodiments, approaches that attempt to select better initial parametersare used. Approaches include use of parameter values from otherconcurrent or prior PC-TCP sessions, parameters determined fromcharacteristics of the communication channel, for example, selected fromstored parameters associated with different types of channels, orparameters determined by the source or destination application accordingto the nature of the data to be transported (e.g., batch versus stream).

Tunable Coding

Referring to FIG. 216, in an embodiment in which parameters are “tuned”(e.g., through feedback from a receiver or on other considerations) aserver application 2411 is in communication with a client application2491 via a communication channel 2452. In one example, the serverapplication 2411 may provide a data stream encoding multimedia content(e.g., a video) that is accepted by the client application 2491, forexample, for presentation to a user of the device on which the clientapplication is executing. The channel 2452 may represent what istypically a series of network links, for example including links of oneor more types, including: a link traversing private links on a serverlocal area network, a link traversing the public Internet, a linktraversing a fixed (i.e., wireline) portion of a cellular telephonenetwork, and a link traversing a wireless radio channel to the user'sdevice (e.g., a cellular telephone channel or satellite link or wirelessLAN).

The channel 2452 may be treated as carrying a series of data units,which may but do not necessarily correspond directly to InternetProtocol (IP) packets. For example, in some implementations multipledata units are concatenated into an IP packet, while in otherimplementations, each data unit uses a separate IP packet or only partof an IP packet. It should be understood that in yet otherimplementations, the Internet Protocol is not used—the techniquesdescribed below do not depend on the method of passing the data unitsover the channel 2452.

A transmitter 2421 couples the server application 2411 to the channel2452, and a receiver 2481 couples the channel 2452 to the clientapplication 2491. Generally, the transmitter 2421 accepts input dataunits from the server application 2481. In general, these data units arepassed over the channel 2452, as well as retained for a period of timein a buffer 2423. From time to time, an error control (EC) component2425 may compute a redundancy data unit from a subset of the retainedinput data units in the buffer 2423, and may pass that redundancy dataunit over the channel 2452. The receiver 2481 accepts data units fromthe channel 2452. In general, the channel 2452 may erase and reorder thedata units. Erasures may correspond to “dropped” data units that arenever received at the receiver, as well as corrupted data units that arereceived, but are known to have irrecoverable errors, and therefore aretreated for the most part as dropped units. The receiver may retain ahistory of received input data units and redundancy data units in abuffer 2483. An error control component 2485 at the receiver 2481 mayuse the received redundancy data units to reconstruct erased input dataunits that may be missing in the sequence received over the channel. Thereceiver 2481 may pass the received and reconstructed input data unitsto the client application. In general, the receiver may pass these inputdata units to the client application in the order they were received atthe transmitter.

In general, if the channel has no erasures or reordering, the receivercan provide the input data units to the client application with delayand delay variation that may result from traversal characteristics ofthe channel. When data units are erased in the channel 2452, thereceiver 2481 may make use of the redundancy units in its buffer 2483 toreconstruct the erased units. In order to do so, the receiver may haveto wait for the arrival of the redundancy units that may be useful forthe reconstruction. The way the transmitter computes and introduces theredundancy data units generally affects the delay that may be introducedto perform the reconstruction.

The way the transmitter computes and introduces the redundancy dataunits as part of its forward error correction function can also affectthe complexity of the reconstruction process at the receiver, and theutilization of the channel. Furthermore, regardless of the nature of theway the transmitter introduces the redundancy data units onto thechannel, statistically there may be erased data units for which there isinsufficient information in the redundancy data units to reconstruct theerased unit. In such cases, the error control component 2485 may requesta retransmission of information from the error control component 2425 ofthe transmitter 2421. In general, this retransmitted information maytake the form of further redundancy information that depends on theerased unit. This retransmission process introduces a delay before theerased unit is available to the receiver. Therefore, the way thetransmitter introduces the redundancy information also affects thestatistics such as how often retransmission of information needs to berequested, and with it the delay in reconstructing the erased unit thatcannot be reconstructed using the normally introduced redundancyinformation.

In some embodiments, the error control component 2485 may provideinformation to the error control component 2425 to affect the way thetransmitter introduces the redundancy information. In general, thisinformation may be based on one or more of the rate of (or moregenerally the pattern of) erasures on units on the channel, rate of (ormore generally timing pattern of) and the state of the available unitsin the buffer 2483 and/or the state of unused data in the clientapplication 2491. For example, the client application may provide a“play-out time” (e.g., in milliseconds) of the data units that thereceiver has already provided to the client application such that if thereceiver were to not send any more units, the client application wouldbe “starved” for input units at that time. Note that in otherembodiments, rather than or in addition to receiving information fromthe receiver, the error control component 2425 at the transmitter mayget feedback from other places, for example, from instrumented nodes inthe network that pass back congestion information.

Referring to FIG. 217, a set of exemplary ways that the transmitterintroduces the redundancy data units into the stream of units passedover the channel makes use of alternating runs of input data units andredundancy data units. In FIG. 217, the data units that are “in flight”on the channel 2452 are illustrated passing from left to right in thefigure. The transmitter introduces the units onto the channel assequences of p input units alternating with sequences of q redundancyunits. Assuming that the data units are the same sizes, this correspondsto a rate R=p/(p+q) code. In an example with p=4 and q=2 and the codehas rate R=2/3.

In a number of embodiments, the redundancy units are computed as randomlinear combinations of past input units. Although the description belowfocuses on such approaches, it should be understood that the overallapproach is applicable to other computations of redundancy information,for example, using low density parity check (LDPC) codes and other errorcorrection codes. In the approach shown in FIG. 217, each run of qredundancy units is computed as a function of the previous D inputunits, where in general but not necessarily D>p. In some cases, the mostrecent d data units transmitted are not used, and therefore theredundancy data units are computed from a window of D−d input dataunits. In FIG. 217, d=2, D=10, and D−d=8. Note that because D−d>p, thewindows of input data units used for computation of the successive runsof redundancy units overlap, such that any particular input data unitwill in general contribute to redundancy data units in more than one ofthe runs of q units on the channel.

In FIG. 217, as well as in FIGS. 218-219 discussed below, buffered inputdata units (i.e., in buffer 2423 shown in FIG. 216) are shown on theleft with time running from the bottom (past) to the top (future), witheach set of D-d units used to compute a run of q redundant unitsillustrated with arrows. The sequence of transmitted units, consistingof runs of input data units alternating with runs of redundant units, isshown with time running from right to left (i.e., later packets on theleft). Data units that have been received and buffered at the receiverare shown on the right (oldest on the bottom), redundant units computedfrom runs of D-d input units indicated next to arrows representing theranges of input data units used to compute those data units. Data unitsand ranges of input data units that have not yet been received areillustrated using dashed lines.

FIGS. 218 and 219 show different selections of parameters. In FIGS. 218,p=2 and q=1 and the code has a rate R=2/3, which is the same rate at theselection of parameters in FIG. 217. Also, as in the FIG. 217 selection,d=2, D=10, and D-d=8. Therefore, a difference between FIG. 217 and FIG.218 is not necessarily a degree of forward error protection (althoughthe effect of burst erasures may be somewhat different in the twocases). More importantly, the arrangement in FIG. 218 generally providesa lower delay from the time of an erased data unit to the arrival ofredundancy information to reconstruct that unit, as compared to thearrangement in FIG. 217. On the other hand, the complexity of processingat the receiver may be greater in the arrangement of FIG. 218 ascompared to the arrangement of FIG. 216, in part because redundancyunits information uses multiple different subsets of the input dataunits, which may require more computation when reconstructing an eraseddata unit. Turning to FIG. 219, at another extreme, a selection ofparameters uses longer blocks with a selection D=8 and q=4. Again, thiscode has a rate R=2/3. In general, this selection of parameters willincur greater delay in reconstruction of an erased data unit as comparedto the selections of parameters shown in FIGS. 217 and 218. On the otherhand, reconstruction of up to four erasures per block of D=8 input dataunits is relatively less complex than would be required by theselections shown in FIGS. 217 and 218.

For a particular rate of code (e.g., rate R=2/3), in an example,feedback received may result in changes of the parameters, for example,between (p,q)=(2,1) or (4,2) or (8,4) depending on of the amount of databuffered at the receiver, and therefore depending on the tolerance ofthe receiver to reconstruction delay.

Note that it is not required that q=p(1−R)/R is an integer, as it is inthe examples shown in FIGS. 25-27. In some embodiments, the length ofthe run of redundant units varies between q=┌p(1−R)/R┐ and q=└p(1−R)/R┘so that the average is ave(q)=p(1−R)/R.

In a variant of the approach described above, different input data unitshave different “priorities” or “importances” such that they areprotected to different degrees than other input data units. For example,in video coding, data units representing an independently coded videoframe may be more important than data units representing adifferentially encoded video frame. For example, if the priority levelsare indexed i=1, 2, . . . , then a proportion ρ_(i)≤1, whereΣ_(i)ρ_(i)=1, of the redundancy data units may be computed using dataunits with priority ≤i. For example, for a rate R code, with blocks ofinput data units of length p, on average ρ₁p(1−R)/R redundancy dataunits per block are computed from input data units with priority ≤i.

The value of D should generally be no more than the target playout delayof the streaming application minus an appropriate margin forcommunication delay variability. The playout delay is the delay betweenthe time a message packet is transmitted and the time it should beavailable at the receiver to produce the streaming application output.It can be expressed in units of time, or in terms of the number ofpackets transmitted in that interval. D can be initially set based onthe typical or desired playout delay of the streaming application, andadapted with additional information from the receiver/application.Furthermore, choosing a smaller value reduces the memory and complexityat the expense of erasure correction capability.

The parameter d specifies the minimum separation between a messagepacket and a parity involving that message packet. Since a parityinvolving a message packet that has not yet been received is not usefulfor recovering earlier message packets involved in that parity, settinga minimum parity delay can improve decoding delay when packet reorderingis expected/observed to occur, depending partly also on the parityinterval.

Referring to FIG. 220, in an example implementation making use of theapproaches described above, the server application 2411 is hosted withthe transmitter 2421 at a server node 810, and the client application2491 is hosted at one or a number of client nodes 891 and 892. Althougha wide variety of types of data may be transported using the approachesdescribed above, one example is streaming of encoded multimedia (e.g.,video and audio) data. The communication channel 2452 (see FIG. 216) ismade up in this illustration as a path through one or more networks851-852 via nodes 861-862 in those respective networks. In someimplementations, the receiver is hosted at the client node 891 beinghosted on the same device as the client application 490.

Cross-Session Parameter Control

In some embodiments, the control of transport layer sessions usesinformation across connections, for example, across concurrent sessionsor across sessions occurring at different times.

Standard TCP implements end-to-end congestion control based onacknowledgments. A new TCP connection that has started up but not yetreceived any acknowledgments uses initial configurable values for thecongestion window and retransmission timeout. These values may be tunedfor different types of network settings.

Some applications, for instance web browser applications, may usemultiple connections between a client application (e.g., the browser)and a server application (e.g., a particular web server application at aparticular server computer). Conventionally, when accessing theinformation to render a single web “page,” the client application maymake many separate TCP sessions between the client and server computers,and using conventional TCP control, each session is controlledsubstantially independently. This independent control includes separatecongestion control.

One approach to addressing technical problems that are introduced byhaving such multiple sessions is the SPDY Protocol (see, e.g., SPDYProtocol—Draft 3.1, accessible athttp://www.chromium.org/spdy/spdy-protocol/spdy-protocol-draft3-1). TheSPDY protocol is an application layer protocol that manipulates HTTPtraffic, with particular goals of reducing web page load latency andimproving web security. Generally, SPDY effectively provides a tunnelfor the HTTP and HTTPS protocols. When sent over SPDY, HTTP requests areprocessed, tokenized, simplified and compressed. The resulting trafficis then sent over a single TCP session, thereby avoiding problems andinefficiencies involved in use of multiple concurrent TCP sessionsbetween a particular client and server computer.

In a general aspect, a communication system maintains informationrelated to communication between computers or network nodes. Forexample, the maintained information can include bandwidth to and/or fromthe other computer, current or past congestion window sizes, pacingintervals, packet loss rates, round-trip time, timing variability, etc.The information can include information for currently active sessionsand/or information about past sessions. One use of the maintainedinformation may be to initialize protocol parameters for a new sessionbetween computers for which information has been maintained. Forexample, the congestion window size or a pacing rate for a new TCP orUDP session may be initialized based on the congestion window size,pacing interval, round-trip time and loss rate of other concurrent orpast sessions.

Referring to FIG. 221, communication system 1200 maintains informationregarding communication sessions between endpoints. For example, thesecommunication sessions pass via a network 1250, and may pass between aserver 1210, or a proxy 1212 serving one or more servers 1214, and aclient 1290. In various embodiments, this information may be saved invarious locations. In some implementations, the client 1290 maintainsinformation about current or past connections. This information may bespecific to the particular server 1210 or the proxy 1212. Thisinformation may also include aggregated information. For example, in thecase of a smartphone on a cellular telephone network, some of theinformation may be generic to connections from multiple servers and mayrepresent characteristics imposed by the cellular network rather than aparticular path to the server 1210. In some implementations, the server1210 or the proxy 1212 may maintain the information based on its pastcommunication with particular clients 1290. In some examples, theclients and servers may exchange the information such that is itdistributed throughout the system 1200. In some implementations, theinformation may be maintained in databases that are not themselvesendpoints for the communication sessions. For instance, it may bebeneficial for a client without relevant stored information to retrieveinformation from an external database.

In one use scenario, when the client 1290 seeks to establish acommunication session (e.g., a transport layer protocol session), itconsults its communication information 1295 to see if it has currentinformation that is relevant to the session it seeks to establish. Forexample, the client may have other concurrent sessions with a serverwith which it wants to communicate, or with which it may have recentlyhad such sessions. As another example, the client 1290 may useinformation about other concurrent or past sessions with other servers.When the client 1290 sends a request to the server 1210 or the proxy1212 to establish a session, relevant information for that session isalso made available to one or both of the endpoints establishing thesession. There are various ways in which the information may be madeavailable to the server. For example, the information may be includedwith the request itself. As another example, the server may request theinformation if it does not already hold the information in itscommunication information 1215. As another example, the server mayrequest the information from a remote or third party database, which hasbeen populated with information from the client or from servers thathave communicated with the client. In any case, the communicationsession between the client and the server is established usingparameters that are determined at least in part by the communicationinformation available at the client and/or server.

In some examples, the communication session may be established usinginitial values of packet pacing interval, congestion window,retransmission timeout and forward error correction. Initial valuessuitable for different types of networks (e.g., Wi-Fi, 4G), networkoperators and signal strength can be prespecified, and/or initial valuesfor successive connections can be derived from measured statistics ofearlier connections between the same endpoints in the same direction.For example: The initial congestion window can be increased from itsdefault value if the packet throughput of the previous connection issufficiently larger than the ratio of the default initial congestionwindow to the minimum round-trip time of the previous connection. Thecongestion window can subsequently be adjusted downwards if the initialreceived acks from the new connection indicate that the available ratehas decreased compared to the previous connection.

The initial pacing interval can be set e.g., as MAX (k1*congestionwindow/previous round-trip time, k2/previous packet throughput), wherek1 and k2 are configurable parameters, or, with receiver pacing, as k*previous pacing interval, where k increases with the loss rate of theprevious connection.

Forward error correction parameters such as code rate can be set ask*previous loss rate, where k is a configurable parameter. The initialretransmission timeout can be increased from its default value if theminimum round-trip time of the previous connection is larger.

Multi-Path

FIG. 222 shows the use of multiple paths between the server and clientto deliver the packet information. These multiple paths may be oversimilar or different network technologies with similar or differentaverage bandwidth, round trip delay, packet jitter rate, packet lossrate and cost. Examples of multiple paths include wired/fiber networks,geostationary, medium and low earth orbit satellites, WiFi, and cellularnetworks. In this example, the transmission control layer can utilize asingle session to distribute the N packets in the block beingtransmitted over the multiple paths according to a variety of metrics(average bandwidth of each path, round trip delay of each path, packetjitter rate, packet loss rate of each path, and cost). The N packets tobe transmitted in each block can be spread across each path in a mannerthat optimizes the overall end-to-end throughput and costs betweenserver and client. The number of packets sent on each path can bedynamically controlled such that the average relative proportions ofpackets sent on each path are in accordance with the average relativeavailable bandwidths of the paths, e.g., using back pressure-typecontrol whereby packets are scheduled so as to approximately equalizequeue lengths associated with the different paths.

For each path, the algorithms described above that embody transmissionand congestion control, forward error correction, sender based pacing,receiver based pacing, stream based parameter tuning, detection andcorrection for missing and out of order packets, use of informationacross multiple TCP connections, fast connection start and stop, TCP/UDPfallback, cascaded coding, recoding by intermediate nodes, and coding ofthe ACKs can be employed to improve the overall end-to-end throughputover the multiple paths between the source node and destination node.When losses are detected and FEC is used, the extra coded packets can besent over any or all of the paths. For instance, coded packets sent torepair losses can be sent preferentially over lower latency paths toreduce recovery delay. The destination node will decode any N of packetsthat are received over all of the paths and assemble them into a blockof N original packets by recreating any missing packets from the onesreceived. If less than N different coded packets are received across allpaths, then the destination node will request the number of missingpackets x where x=N−number of packets received be retransmitted. Any setof x different coded packet can be retransmitted over any path and thenused to reconstruct the missing packets in the block of N.

When there are networks with large differences in round trip time (RTT)latencies, the packets received over the lower RTT latencies will needto be buffered at the receiver in order to be combined with the higherRTT latency packets. The choice of packets sent on each path can becontrolled so as to reduce the extent of reordering and associatedbuffering on the receiver side, e.g., among the packets available to besent, earlier packets can be sent preferentially on higher latency pathsand later packets can be sent preferentially on lower latency paths.

Individual congestion control loops may be employed on each path toadapt to the available bandwidth and congestion on the path. Anadditional overall congestion control loop may be employed to controlthe total sending window or rate across all the paths of a multi-pathconnection, for fairness with single-path connections.

Referring to FIG. 223, a communication system utilizes a first, asatellite data path 3102 having a relatively high round trip timelatency and a second, a DSL data path 3104 having a relatively low roundtrip time latency. When a user application 3106 sends a request tostream video content, a content server 3108 (e.g., video streamingservice) provides some or all of the requested video content to a remoteproxy 3110 which generates encoded video content 3112 for transmissionto the user application 3106. Based on the RTT latencies of the firstdata path 3102 and the second data path 3104, the remote proxy 3110splits the encoded video content 3112 into an initial portion 3114(e.g., the first 5 seconds of video content) and a subsequent portion3116 (e.g., the remaining video content). The remote proxy 3110 thencauses transmission of the initial portion 3114 over the second, the lowlatency data path 3104 and transmission of the subsequent portion 3116over the first, of the high latency data path 3102.

Referring to FIG. 224, due to the lower latency of the second data path3104, the initial portion 3114 of the video content arrives at a localproxy 3118 quickly, where it is decoded and sent to the user application3106 for presentation to a viewer. The subsequent portion 3116 of thevideo content is still traversing the first, high latency data path 3102at the time that presentation of the initial portion 3114 of the videocontent to the viewer commences.

Referring to FIG. 225, during presentation of the decoded initialportion 3114 of video content to the viewer, the subsequent portion 3116of the video content arrives at the local proxy 3118 where it is decodedand sent to the user application 3106 before presentation of the initialportion 3114 of the video content to the viewer is complete. In someexamples, sending the initial portion 3114 of the video content over thelow latency data path 3104 and sending the subsequent portion 3116 ofthe video content over the high latency data path 3102 avoids lengthywait times between when a user requests a video and when the user seesthe video (as would be the case if using satellite only communication)while minimizing data usage over the low latency data path (which may bemore costly to use).

In some examples, other types of messages may be preferentially sentover the low latency data path. For example, acknowledgement messages,retransmission messages, and/or other time critical messages may betransmitted over the low latency data path while other data messages aretransmitted over the higher latency data path.

In some examples, additional data paths with different characteristics(e.g., latencies) can also be included in the communication system, withmessages being balanced over any of a number of data paths based oncharacteristics of the messages (e.g., message type) and characteristicsof the data paths.

In some examples, other types of messages may be preferentially sentover the low latency data path. For example, acknowledgement messages,retransmission messages, and/or other time critical messages may betransmitted over the low latency data path while other data messages aretransmitted over the higher latency data path.

In some examples, additional data paths with different characteristics(e.g., latencies) can also be included in the communication system, withmessages being balanced over any of a number of data paths based oncharacteristics of the messages (e.g., message type) and characteristicsof the data paths.

Alternatives and Implementations

In the document above, certain features of the packet coding andtransmission control protocols are described individually, or inisolation, but it should be understood that there are certain advantagesthat may be gained by combining multiple features together. Preferredembodiments for the packet coding and transmission control protocolsdescribed may depend on whether the transmission links and network nodestraversed between communication session end-points belong to certainfiber or cellular carriers (e.g., AT&T, T-Mobile, Sprint, Verizon, Level3) and/or end-user Internet Service Providers (ISPs) (e.g., AT&T,Verizon, Comcast, Time Warner, Century Link, Charter, Cox) or are overcertain wired (e.g., DSL, cable, fiber-to-the-curb/home (FTTx)) orwireless (e.g., WiFi, cellular, satellite) links. In embodiments, probetransmissions may be used to characterize the types of network nodes andtransmission links communication signals are traversing and the packetcoding and transmission control protocol may be adjusted to achievecertain performance. In some embodiments, data transmissions may bemonitored to characterize the types of network nodes and transmissionlinks communication signals are traversing and the packet coding andtransmission control protocol may be adjusted to achieve certainperformance. In at least some embodiments, quantities such as RTT,one-way transmission times (OWTT), congestion window, pacing rate,packet loss rate, number of overhead packets, and the like may bemonitored continuously, intermittently, in response to a trigger signalor event, and the like. In at least some embodiments, combinations ofprobe transmissions and data transmissions may be used to characterizenetwork and communication session performance in real time.

In at least some embodiments, network and communication parameters maybe stored in the end-devices of communication sessions and/or they maybe stored in network resources such as servers, switches, nodes,computers, databases and the like. These network and communicationparameters may be used by the packet coding and transmission controlprotocol to determine initial parameter settings for the protocol toreduce the time it may take to adjust protocol parameters to achieveadequate performance. In embodiments, the network and communicationparameters may be tagged and/or associated with certain geographicallocations, network nodes, network paths, equipment types, carriernetworks, service providers, types of transmission paths and the like.In embodiments, the end-devices may be configured to automaticallyrecord and/or report protocol parameter settings and to associate thosesettings with certain locations determined using GPS-type locationidentification capabilities resident in those devices. In embodiments,the end-devices may be configured to automatically record and/or reportprotocol parameters settings and to associate those settings withcertain carrier networks, ISP equipment traversed, types of wired and/orwireless links and the like.

In at least some embodiments, a packet coding and transmission controlprotocol as described above may adjust more than one parameter toachieve adequate or improved network performance. Improved networkperformance may be characterized by less delay in delivering datapackets, less delay in completing file transfers, higher quality audioand video signal delivery, more efficient use of network resources, lesspower consumed by the end-users, more end-users supported by existinghardware resources and the like.

In at least some embodiments, certain modules or features of the packetcoding and transmission control protocol may be turned on or offdepending on the data's path through a network. In some embodiments, theorder in which certain features are implemented or controlled may beadjusted depending on the data's path through a network. In someembodiments, the probe transmissions and/or data transmissions may beused in open-loop or closed-loop control algorithms to adjust theadjustable parameters and/or the sequence of feature implementation inthe packet coding and transmission control protocol.

It should be understood that examples which involve monitoring tocontrol the protocol can in general involve aspects that are implementedat the source, the destination, or at a combination of the source andthe destination. Therefore, it should be evident that althoughembodiments are described above in which features are described as beingimplemented at particular endpoints, alternative embodiments involveimplementation of those features at different endpoints. Also, asdescribed above, monitoring to control the protocol can in generalinvolve aspects that are implemented intermediate nodes or points in thenetwork. Therefore, it should be evident that although embodiments aredescribed above in which features are described as being implemented atparticular endpoints, alternative embodiments involve implementation ofthose features at different nodes, including intermediate nodes,throughout the network.

In addition to the use of monitored parameters for control of theprotocols, the data may be used for other purposes. For example, thedata may support network analytics that are used, for example, tocontrol or provision the network as a whole.

The PC-TCP approaches may be adapted to enhance existing protocols andprocedures, and in particular protocols and procedures used in contentdelivery, for example, as used in coordinated content delivery networks.For instance, monitored parameters may be used to direct a client to theserver or servers that can deliver an entire unit of content as soon aspossible rather than merely direct the client to a least loaded serveror to server accessible over a least congested path. A difference insuch a new approach is that getting an entire file as fast as possiblemay require packets to be sent from multiple servers and/or servers thatare not geographically the closest, over multiple links, and using newacknowledgement protocols that coordinate the incoming data whilerequiring a minimum of retransmissions or FEC overhead. Coordinating mayinclude waiting for gaps in strings of packets (out-of-order packets) tobe filled in by later arriving packets and/or by coded packets. Inaddition, the PC-TCP approaches may improve the performance of wireless,cellular, and satellite links, significantly improving the end-to-endnetwork performance.

Some current systems use “adaptive bit rates” to try to preserve videotransmission through dynamic and/or poorly performing links. In someinstances, the PC-TCP approaches described above replace adaptive bitrate schemes and may be able to present a very high data rate to a userfor a long period of time. In other instances, the PC-TCP approaches areused in conjunction with currently-available adaptive bit rate schemesto support higher data rates on average than could be supported byadaptive bit rate schemes alone. In some instances, the PC-TCPapproaches may include integrated bit rate adjustments as part of itsfeature set and may use any and/or all of the previously identifiedadjustable parameters and/or monitored parameters to improve theperformance of a combined PC-TCP and bit-rate adaptive solution.

Certain embodiments described following relate to heating, and moreparticularly to cooking and recipes, including by use of intelligentdevices, and in a context of the IoT.

With the emergence of the IoT, opportunities exist for unlocking valuesurrounding a wide range of devices. To date, such opportunities havebeen limited for many users, particularly in rural areas of developingcountries, by the absence of robust energy and communicationsinfrastructure. The same problems with infrastructure also limit theability of users to access more basic features of certain devices; forexample, rather than using modern cooking systems, such as with gasburners, many rural users still cook over fires, using wood or otherbiofuel. A need exists for devices that meet basic needs, such as formodern cooking capability, without reliance on infrastructure, and anopportunity exists to expand the capabilities of basic cooking devicesto provide a much wider range of capabilities that will serve otherneeds and provide other benefits to users of cooking devices.

Many industrial environments are similarly isolated from conventionalenergy and communications infrastructure. For example, offshore drillingplatforms, industrial mining environments, pipeline operations,large-scale agricultural environments, marine exploration environments(e.g., deep ocean exploration), marine and other large-scaletransportation environments (such as ships, boats, submarines, aircraftand spacecraft) are often entirely isolated from the traditional powergrid, or require very expensive power transmission cables to receivepower from traditional sources. Other industrial environments areisolated for other reasons, such as to maintain “clean room” isolationduring semi-conductor manufacturing, pharmaceutical preparation, orhandling of hazardous materials, where interfaces like outlets andswitches for delivering conventional power potentially provide points ofpenetration or escape for contaminants or biologically active materials.For these environments, a need exists for cooking systems that provideimproved independence from conventional power sources. Also, in many ofthese environments fire is a significant hazard, among other thingsbecause of the presence of fire hazards and significant restrictions onegress for personnel. In those cases, storage of fuel for cooking in anenvironment presents a risk, because the fuel can exacerbate the extentof a fire, potentially resulting in disastrous consequences.Accordingly, such platforms and environments, such as oil drillingplatforms, may use diesel generators to power cooking and other systems,because diesel is perceived as presenting lower risk than propane,gasoline, or other fuel sources; however, diesel fuel also burns andremains a significant hazard. A need exists for safer mechanisms forproviding cooking capability in isolated industrial environments.

Intelligent cooking systems are disclosed herein, including anintelligent cooking system that is provided with processing,communications, and other information technology components, for remotemonitoring and control and various value-added features and services,embodiments of which use an electrolyzer, optionally a solar-poweredelectrolyzer, to produce hydrogen as an on-demand fuel stream for aheating element, such as a burner, of the cooking system.

Embodiments of cooking systems disclosed herein include ones forconsumer and commercial use, such as for cooking meals in homes and inrestaurants, which may include various embodiments of cooktops, stoves,toasters, ovens, grills and the like. Embodiments of cooking systemsalso include industrial cooking systems, such as for heating, drying,curing, and cooking not only food products and ingredients, but also awide variety of other products and components that are manufactured inand/or used in the industrial environments. These may include systemsand components used in assembly lines (such as for heating, drying,curing, or otherwise treating parts or materials at one stage ofproduction, such as to treat coatings, polymers, or the like that arecoated, dispersed, painted, or otherwise disposed on components), insemi-conductor manufacturing and preparation (such as for heating orcuring layers of a semi-conductor process, including in robotic assemblyprocesses), in tooling processes (such as for curing injection molds andother molds, tools, dies, or the like), in extrusion processes (such asfor curing, heating or otherwise treating results of extrusion), andmany others. These may also include systems and components used invarious industrial environments for servicing personnel, such as onships, submarines, offshore drilling platforms, and other marineplatforms, on large equipment, such as on mining or drilling equipment,cranes, or agricultural equipment, in energy production environments,such as oil, gas, hydro-power, wind power, solar power, and otherenvironments. Accordingly, while certain embodiments are disclosed forspecific environments, references to cooking systems should beunderstood to encompass any of these consumer, commercial and industrialsystems for cooking, heating, curing, and treating, except where contextindicates otherwise.

Provided herein is an intelligent cooking system leveraging hydrogentechnology plus cloud-based value-added-services derived from profiling,analytics, and the like. The smart hydrogen technology cooking systemprovides a standardized framework enabling other intelligent devices,such as smart-home devices and IoT devices to connect to the platform tofurther enrich the overall intelligence of contextual knowledge thatenables providing highly relevant value-added-services. The intelligentcooking system device (referred to herein in some cases as the“cooktop”), may be enabled with processing, communications, and otherinformation technology components and interfaces for enabling a varietyof features, benefits, and value added services including ones based onuser profiling, analytics, remote monitoring, remote processing andcontrol, and autonomous control. Interfaces that allowmachine-to-machine or user-to-machine communication with other devicesand the cloud (such as through application programming interfaces)enables the cooking system to contribute data that is valuable foranalytics (e.g., on users of the cooking system and on various consumer,commercial and industrial processes that involve the cooking system), aswell as for monitoring, control and operation of other devices andsystems. Through similar interfaces, the monitoring, control and/oroperation of the cooking system, and its various capabilities, canbenefit from or be based on data received from other devices (e.g., IoTdevices) and from other data sources, such as from the cloud. Forexample, the cooking system may track its usage, such as to determinewhen to send a signal for refueling the cooking system itself, to send asignal for re-supplying one or more ingredients, components or materials(such as based on detected patterns of usage of the same over timeperiods), to determine and provide guidance on usage of the cookingsystem (such as to suggest training or improvements in usage to improveefficiency or efficacy), and the like. These may include results basedon applying machine learning to the use of the fuel, the use of thecooking system, or the like.

In embodiments, the intelligent cooking system may be fueled by ahydrogen generator, referred to herein in some cases as theelectrolyzer, an independent fuel source that does not requiretraditional connections to the electrical power grid, to sources of gas(e.g., natural gas lines), or to periodic sources of supply ofconventional fuels (such as refueling oil, propane, diesel, or otherfuel tanks). The electrolyzer may operate on a water source to separatehydrogen and oxygen components and subsequently provide the hydrogencomponent as a source of fuel, such as an on-demand source of fuel, forthe intelligent cooking system. In embodiments, the electrolyzer may bepowered by a renewable energy source, such as a solar power source, awind power source, a hydropower source, or the like, thereby providingcomplete independence from the need for traditional powerinfrastructure. Methods and systems describing the design,manufacturing, assembly, deployment, and use of an electrolyzer areincluded herein. Among other benefits, the electrolyzer allows storageof water, rather than flammable materials like oil, propane, and diesel,as a source of energy for powering cooking systems in various isolatedor sensitive industrial environments, such as on or in ships,submarines, drilling platforms, mining environments, pipelineenvironments, exploration environments, agricultural environments, cleanroom environments, air- and space-craft environments, and others.Intelligent features of the cooking system can include control of theelectrolyzer, such as remote and/or autonomous control, such as toprovide a precise amount of hydrogen fuel (converted from water) at theexact point and time it is required. In embodiments, mechanisms may beprovided for capturing and returning products of the electrolyzer, suchas to return any unused hydrogen and oxygen into water form (ordirecting them for other use, such as using them as a source of oxygenfor breathing).

Methods and systems describing the design, manufacturing, assembly,deployment, and use of a smart hydrogen-based cooking system areincluded herein. Processing hardware and software modules for operatingvarious capabilities of the cooking system may be distributed, such ashaving modules or components that are located in sub-systems of thecooking system (e.g., the burners or other heating elements, temperaturecontrols, or the like), having modules or components located inproximity to a user interface for the cooking system (e.g., associatedwith a control panel), having modules or components located in proximityto a communications port for the cooking system (e.g., an integratedwireless access point, cellular communications chip, or the like, or adocking port for a communications devices, such as a smart phone),having modules or components located in nearby devices, such as othersmart devices (e.g., a NEST® thermostat), gateways, access points,beacons, or the like, and/or having modules or components located onservers, such as in the cloud or in a hosted remote control facility.

In embodiments, the cooking system may have a mobile docking facility,such as for docking a smart phone or other control device (such as aspecialized device used in an industrial process, such as aprocessor-enabled tool or piece of equipment), which may include powerfor charging the smart phone or other device, as well as datacommunications between the cooking system and the smart phone, such asto allow the smart phone to be used (such as via an app, browserfeature, or control feature of the phone) as a controller for thecooking system.

In embodiments, the cooking system may include various hardwarecomponents, which may include associated sensors for monitoringoperation, processing and data storage capabilities, and communicationcapabilities. The hardware components may include one or more burners orheating elements, (e.g., gas burners, electric burners, inductionburners, convection elements, grilling elements, radiative elements, andthe like), one or more fuel conduits, one or more level indicators forindicating fuel level, one or more safety detectors (such as gas leakdetectors, temperature sensors, smoke detectors, or the like). Inembodiments, a gas burner may include an on-demand gas-LPG hybridburner, which can burn conventional fuel like liquid propane, but whichcan also burn fuel generated on demand, such as hydrogen produced by theelectrolyzer. In embodiments, the burner may be a consumer cooktopburner having an appropriate power capability, such as being able toproduce 20,000 British Thermal Unit (“BTU”).

In embodiments, the cooking system may include a user interface thatfacilitates intuitive, contextual, intelligence-driven, and personalizedexperience, embodied in a dashboard, wizard, application interface(optionally including or integrating with one more associated smartphonetablet or browser-based applications or interfaces for one or more IoTdevices), control panel, touch screen display, or the like. The userinterface may include distributed components as described above forother software and hardware components. The application interface mayinclude interface elements appropriate for cooking foods (such arerecipes) or for using the cooking system for various consumer,commercial or industrial processes (such as recipes for makingsemi-conductor elements, for curing a coating or injection mold, andmany others).

Methods and systems describing the design, manufacturing, assembly,deployment and use of a solar-powered hydrogen production facility inconjunction with a hydrogen-based cooking system are included herein.

Methods and systems describing the design, manufacturing, assembly,deployment and use of a commercial hydrogen-based cooking system that issuitable for use in a range of restaurants, cafeterias, mobile kitchens,and the like are included herein.

Methods and systems describing the design, manufacturing, assembly,deployment and use of an industrial hydrogen-based cooking system thatis suitable for use as a food cooking system in various isolatedindustrial environments are included herein.

Methods and systems describing the design, manufacturing, assembly,deployment and use of an industrial hydrogen-based cooking system thatis suitable for use as a heating, drying, curing, treating or othercooking system in various industrial environments are included herein,such as for manufacturing and treating components and materials inindustrial production processes, including automated, robotic processesthat may include system elements that connect and coordinate with theintelligent cooking system, including in machine-to-machineconfigurations that enable application of machine learning to thesystem.

Methods and systems describing the design, manufacturing, assembly,deployment and use of a low-pressure hydrogen storage system aredescribed herein. The low-pressure hydrogen storage system may becombined with solar-powered hydrogen generation. In embodiments, thecooking system may receive fuel from the low-pressure hydrogen storagetank, which may safely store hydrogen produced by the electrolyzer. Inembodiments, the hydrogen may be used immediately upon completion ofelectrolyzing, such that no or almost no hydrogen fuel needs to bestored.

Methods and systems describing the architecture, design, andimplementation of a cloud-based platform for providingvalue-added-services derived from profiling, analytics, and the like inconjunction with a smart hydrogen-based cooking system are includedherein. The cloud-based platform may further provide communications,synchronization among smart-home devices and third parties, security ofelectronic transactions and data, and the like. In embodiments, thecooking system may comprise a connection to a smart home, including toone or more gateways, hubs, or the like, or to one or more IoT devices.The cooking system may itself comprise a hub or gateway for other IoTdevices, for home automation functions, commercial automation functions,industrial automation functions, or the like.

Methods and systems describing an intelligent user interface for acloud-based platform for providing value-added services (“VAS”) inconjunction with a smart hydrogen-based cooking system are includedherein. The intelligent user interface may comprise an electronic wizardthat may provide a contextual and intelligence driven personalizedexperience dashboard for computing devices that connect to a smart-homenetwork or a commercial or industrial network based around the smarthydrogen-based cooking system. The architecture, design andimplementation of the platform may be described herein.

Methods and systems for configuring, deploying, and providing valueadded services via a cloud-based platform that operates in conjunctionwith a smart hydrogen-based cooking system and a plurality ofinterconnected devices (e.g., mobile devices, Internet servers, and thelike) to prepare profiling, analytics, intelligence, and the like forthe VAS are described herein. In embodiments, the cooking system mayinclude various VAS, such as ones delivered by a cloud-based platformand/or other IoT devices. For example, among many possibilities, thecooking system may provide recipes, allow ordering of ingredients,components or materials, track usage of ingredients to prompt re-orders,allow feedback on recipes, provide recommendations for recipes(including based on other users, such as using collaborative filtering),provide guidance on operation, or the like. The architecture, design,and implementation of these methods and systems and of thevalue-added-services themselves may further be described herein.

In embodiments, through a user interface, such as a wizard, variousbenefits, features, and services may be enabled, such as various cookingsystem utilities (e.g., a liquid propane gas gauge utility, a cookingassistance utility, a detector utility (such as for leakage,overheating, or smoke, or the like), a remote control utility, or thelike). Services for shopping (e.g., a shopping cart or food orderingservice), for health (such as providing health indices for foods, andpersonalized suggestions and recommendations), for infotainment (such asplaying music, videos or podcasts while cooking), for nutrition (such asproviding personalized nutrition information, nutritional searchcapabilities, or the like) and shadow cooking (such as providing remotematerials on how to cook, as well as enabling broadcasting of the user,such as in a personalized cooking channel that is broadcast from thecooking system, or the like).

Methods and systems for profiling, analytics, and intelligence relatedto deployment, use, and service of a plurality of hydrogen-based cookingsystems that are deployed in a range of environments including urban,rural, commercial, and industrial settings are described herein. Urbansettings may include rural villages, low cost housing arrangements,apartments, housing projects, and the like where several end users(e.g., individual households, common kitchens, and the like) may bephysically proximal (e.g., apartments in a building, and the like). Thephysical proximity can facilitate shared access to platform components,such as a hydrolyser or low pressure stored hydrogen, and the like. Tothe extent that individual cooktop deployments may communicate throughlocal or Internet-based network access, additional benefits arise aroundtopics such as, planning for demand loading, and the like. An examplemay include generating and storing more hydrogen during the week whenpeople tend to cook a home than on the weekend, or using sharedinformation about recipes to facilitate bulk delivery of fresh items toan apartment building, multiple proximal restaurants, and the like. Inembodiments, the cooking system may enable and benefit from analytics,such as for profiling, recording or analyzing users, usage of thedevice, maintenance and repair histories, patterns relating to problemsor faults, energy usage patterns, cooking patterns, and the like.

These methods and systems may further perform profiling, analytics, andintelligence related to deployment, use and service of solar-poweredelectrolyzers that generate hydrogen that is stored in a low-pressurehydrogen storage system.

Methods and systems related to extending the capabilities and access tocontent and/or VAS of a smart hydrogen-based cooking system throughintelligent networking and development of transaction channels aredescribed herein.

Methods and systems of an ecosystem based around the methods and systemsof generating hydrogen via solar-powered electrolyzers, storing thegenerated hydrogen in low pressure storage systems, distribution and useof the stored hydrogen by one or more individuals, and the like, aredescribed herein. In embodiments, the cooking system, or a collection ofcooking systems, may provide information to a broader businessecosystem, such as informing suppliers of foods or other materials orcomponents of aggregate information about usage, informing advertisers,managers and manufacturers about consumption patterns, and the like.Accordingly, the cooking system may comprise a component of a businessecosystem that includes various parties that provide variouscommodities, information, and devices.

Another embodiment of smart cooking technology described herein mayinclude an intelligent, computerized knob or dial suitable for directuse with any of the cooking systems, probes, single burner and otherheating elements, and the like described herein. Such a smart knob ordial may include all electronics and power necessary for independentoperation and control of the smart systems described herein.

In embodiments, the cooking system is an industrial cooking system usedto provide heat in a manufacturing process. In embodiments, theindustrial cooking system is used in at least one of a semi-conductormanufacturing process, a coating process, a molding process, a toolingprocess, an extrusion process, a pharmaceutical manufacturing processand an industrial food manufacturing process.

In embodiments, a smart knob is adapted to store instructions for aplurality of different cooking systems. In embodiments, a smart knob isconfigured to initiate a handshake with a cooking system based on whichthe knob automatically determines which instructions should be used tocontrol the cooking system. In embodiments, a smart knob is configuredwith a machine learning facility that is configured to improve thecontrol of the cooking system by the smart knob over a period of usebased on feedback from at least one user of the cooking system.

In embodiments, a smart knob is configured to initiate a handshake witha cooking system to access at least one value-added service based on aprofile of a user.

Referring to FIG. 226, an integrated cooktop embodiment 11 of theintelligent cooking system methods and systems 21 described herein isdepicted. The cooktop embodiment 11 of FIG. 226, may include one or moreburners 31 that may burn one or more types of fuel, such as LiquidPropane Gas (LPG), hydrogen, a combination thereof, and the like. Gasburners may, for example, be rated to provide variable heat, includingup to a maximum heat, thereby consuming a corresponding amount of fuel.One or more of the burners 31 may operate with an LPG source 51 and asource of hydrogen gas 61 such that the hydrogen source 61 may beutilized based on a demand for fuel indicated by the burner 31, ameasure of available LPG fuel, an amount of LPG fuel used over time, andany combination of use, demand, historical usage, anticipated usage,availability of supply, weather conditions, calendar date/time (e.g.,time of day, week, month, year, and the like), proximity to an event(e.g., an intense cooking time, such as just before a holiday), and thelike. The hydrogen source 61 may be utilized so that the amount of otherfuel used, such as LPG, is kept below a usage threshold. Such a usagethreshold may be based on costs of LPG gas, uses of LPG gas by otherburners 31 in the cooking system 21, other cooking systems 21 in thevicinity (e.g., other cooking systems 21 in a restaurant, other cookingsystems 21 in nearby residences), and the like. Each cooking system 21and/or burner 31 within the cooking system 21 may therefore provideon-demand fuel sourcing dynamically without need for user input ormonitoring of the cooking system 21. By automating fuel sourcing, theburner may extend the life of available LPG by automatically introducingthe hydrogen fuel, such as by switching from one source to the other orby reducing one source (e.g., LPG) while increasing the other (e.g.,hydrogen). The degree to which each fuel source is utilized may be basedon a set of operational rules that target efficiency, LPG fuelconsumption, availability of hydrogen, and the like. Rating of the oneor more burners 31 may be under the control of a processor, including toprovide different levels of rating for different fuel sources, such asLPG alone, hydrogen alone, or a mixture of LPG and hydrogen with a givenratio of constituent parts.

Each of the burners 31, the cooking systems 21, or collection of thecooking systems 21 may be configured with fuel controls, such as fuelmixing devices (e.g., valves, shunts, mixing chambers, pressurecompensation baffles, check valves, and the like) that may be controlledautomatically based, at least in part on some measure of historical,current, planned, and/or anticipated consumption, availability, and thelike. In an example, one or more burners 31 may be set to produce 1000 Wof heat and a burner gas source control facility may activate one ormore gas mixing devices while monitoring burner output to ensure thatthe burner output does not deviate from the output setting by more thana predefined tolerance, such as 100 W or ten percent (10%).Alternatively, a model of gas consumption and burner output, embodied ina software module that may have access to data sources regarding typesof gas, burning characteristics, types of burners, ratingcharacteristics, and the like, may be used by the control facility toregulate the flow of one or more of gasses being mixed to deliver aconsistent burner heat output. Any combination of burner output sensing,modeling, and preset mixing control may be used by the control facilitywhen operating fuel sourcing and/or mixing devices.

The one or more burners 31 may include intelligence for enhancingoperation, efficiency, fuel conservation, and the like. Each of theburners 31 may have its own control facility 101. A centralized cookingsystem control facility may be configured to manage operation of theburners 31 of the cooking system 21 or other heating elements notedthroughout this disclosure. Alternatively, the individual burner controlfacilities 101 may communicate over a wired and/or wireless interface tofacilitate combined cooking system burner control. One or more sensorsto detect presence of an object in the targeted heating zone (e.g.,disposed on the burner grate) may provide feedback to the controlfacility. Object presence sensors may also provide an indication of thetype, size, density, material, and other aspect of the detected objectin the targeted heating zone. Detection of a material such as metal,versus cloth (e.g., a person's sleeve), versus human flesh mayfacilitate efficiency and safety. When cloth or human flesh is detected,the control facility may inhibit heat production so as to avoid burningthe person's skin or causing their clothing to catch fire. Such acontrol facility safety feature may be over ridden through user input tothe control facility to give the user an opportunity to determine if theinhibited operation is proper. Other detectors, such as spill over(e.g., moisture) detectors in proximity to the burner may help inmanaging safety and operation. A large amount of spillage from a pot maycause the flame being produced by the burner to be extinguished. Basedon operational rules, the source of gas may be disabled and/or anigniter may be activated to resume proper operation of the burner. Otheractions may also be configured into the control facility, such assignaling the condition to a user (e.g., through an indicator on thecooking system 21, via connection to a personal mobile device, to acentral fire control facility, and the like).

Burner control facilities 101 may control burner heat output (andthereby control fuel consumption) based on one or more models ofoperation, such as to heat a pan, pot, component, material, or otheritem placed in proximity to the burner 21 or other heating element. Asan example, if a user wants to boil water in a heavy metal pot quickly,a control facility may cause a burner to produce maximum heat. Based onuser preferences and/or other factors as noted above related to demand,supply, and the like, the control facility may adjust the burner outputwhile notifying the user of a target time for completion of a heatingactivity (e.g., time until the water in the pot boils). In this way anintelligent burner 21 (e.g., on with a burner control facility) mayachieve some user preferences (e.g., heating temperature) whilecompromising on others (e.g., amount of time to boiling, and the like).The parameters (e.g., operational rules) for such tradeoff may beconfigured into the cooking system 21/burner 31 during production, maybe adjustable by the user directly or remotely, may be responsive tochanging conditions, and the like. In embodiments, machine learning,either embodied at the cooking system 21, in the cloud, or in acombination, may be used to optimize the parameters for given objectivessought by users, such as cooking time, quality of the result (e.g.,based on feedback measures about the output product, such as taste inthe case of foods or other quality metrics in the case of other productsof materials). For example, the cooking system 21 may be configuredunder control of machine learning to try different heating patterns fora food and to solicit user input as to the quality of the resultingitem, so that over time an optimal heating pattern is developed.

The intelligent cooking system 21 as described herein and depicted inFIG. 226 may include an interface port 127 with supporting structuralelements to securely hold a personal mobile device 150 (e.g., a mobilephone) in a safe and readily viewable position so that the user may haveboth visual and at least auditory access to the device. The cookingsystem 21 may include features that further ensure that the mountedmobile device 150 is not subject to excessive heat, such as heatshields, deflectors, air flow baffles, heat sinks, and the like. Asource of air-flow may be incorporated to facilitate moving at least aportion of heated air from one or more of the burners 31 away from amounted personal mobile device 150.

The intelligent burner embodiment 280 depicted in FIG. 227 represents asingle burner embodiment 210 of the intelligent cooking system 21described herein. Any, none, or all features of a multi-burnerintelligent cooking system 21 may be configured with the single burnerversion depicted in FIG. 227. Further depicted in FIG. 227 is a versionof the intelligent burner 280 that may have an enclosed burner chamber220 that provides heat in a target heat-zone as a plane of heat ratherthan as a volume of heat. This may be generated by induction,electricity, or the like that may be produced by converting a source offuel, such as LPG and/or hydrogen with a device that may produceelectricity from a combustible gas.

The intelligent cooking system 21 may be combined with a hydrogengenerator 300 to provide a source of hydrogen for use with the burners31 as described herein. FIG. 228 depicts a solar-powered hydrogenproduction and a storage station 320. The hydrogen production station320 may be configured with one or more solar collectors 330, such assunlight-to-electricity conversion panels 340 that may produce energyfor operating an electrolyzer 350 that converts a hydrogen source, suchas water vapor, to at least hydrogen and oxygen for storage. Energy fromthe solar collectors 330 may power one or more of the electrolyzers 350,such as one depicted in the embodiment 700 of FIG. 232. The one or moreof the electrolyzers 350 may process water vapor, such as may beavailable in ambient air, for storage in a storage system 360, such as alow-pressure storage system 370 depicted in FIG. 228. Alternatively,and/or in addition to processing air-born water vapor, a source ofwater, such as collected rainfall, public water supply, or other sourcemay be processed by the electrolyzer 350 to produce hydrogen fuel.

As hydrogen fuel is produced, it may be stored in a suitable storagecontainer, such as the low-pressure storage system 370 that may beconfigured with the solar-powered electrolyzer system 350. The hydrogenproduced by the solar-powered electrolyzer 350 may be routed to one ormore intelligent cooking systems 21 in addition to or in place of beingrouted to the storage system 360. The hydrogen production and storagesystem 320 may produce hydrogen based on a variety of conditionsincluding, without limitation, availability of a source of water vapor,availability of power to the electrolyzer, an amount of sunlight beingcollected, a forecast of sunlight, a demand for hydrogen energy, aprediction of demand, based on availability of LPG, usage of LPG, andthe like.

The low-pressure gas storage system 370 may store the hydrogen andoxygen in ultraviolet (“UV”) coated plastic bags or through waterimmersion technology (e.g., biogas). The maximum pressure inside thesystem may be less than 1.1 bar, which promotes safety, as the pressureis very low. Also, as no compressors are used, the cost for storage ismuch lower than for active storage systems that store compressed gas.FIG. 229, FIG. 230, and FIG. 231 depict an embodiment 400 of such thelow-pressure storage system 370, with an inlet valve 411 and outletvalve 413 providing ports into an interior storage area 415 with theinternal volume separated into two parts.

The low pressure set up may directly work from renewable energy, such assolar energy collected by solar cells, wind energy, hydro-power, or thelike, improving the efficiency. The selected source of renewable energymay be based on characteristics of the environment; for example, marineindustrial environments may have available wind and hydro-power,agricultural environments may have solar power, etc. Also, if therenewable energy (e.g., solar energy) collection facility is connectedto a power grid, the electricity generated and the energy stored may beprovided to the grid, e.g., during high cost periods. Likewise, the gridmay be used to restore any used energy during off peak hours at reducedcosts.

The designed low-pressure storage may be used to store hydrogen, as asource of energy, that may be converted into electricity. The designedsystem may store energy at very low cost and may have a lifetime ofyears, e.g., more than 15 years, which modern batteries do not have.Amounts of storage may be configured to satisfy safety requirements,such as storing little enough to cause a minimal fire hazard as comparedto storing larger amounts of other fuels.

In an embodiment, the intelligent cooking system 21 may signal to theelectrolyzer system 350 a demand for hydrogen fuel. In response, theelectrolyzer system 350 may direct stored hydrogen to the cooking system21, begin to produce hydrogen, or indicate that hydrogen is notcurrently available. This response may be based, at least in part onconditions for producing hydrogen. If conditions for producing hydrogenare good, the electrolyzer system may begin to produce hydrogen fuelrather than merely sourcing it from storage. In this way, thecontemporaneous demand for hydrogen fuel and an ability to produce itmay be combined to determine the operation of the energy production andconsumption systems.

The intelligent cooking system 21 and/or hydrogen production and storagesystems described herein may be combined with a platform that interactswith electronic devices and participants in a related ecosystem ofsuppliers, content providers, service providers, regulators, and thelike to deliver VAS to users of the intelligent cooking system 21, usersof the hydrogen production system, and other participants in theecosystem. Certain features of such a platform 800 may be depicted inFIG. 233. The platform 800, which may be a cloud-based platform, mayhandle cooking system utilities, such as leakage sensing, fuel sourcing,usage assistance, remote control, and the like. In an example, a userwho is located remotely from the intelligent cooking system 21 mayconfigure the cooking system 21 to operate at a preset time, or based ona preset condition from his/her computing device (e.g., a personalmobile phone, desktop computer, laptop, tablet, and the like). The usermay further be notified when the cooking system 21 begins to operate,thereby ensuring the user that the cooking system 21 is operating asexpected. A user or third party (e.g., a regulatory agency, landlord,and the like) may configure one or more present conditions. Suchconditions may include a variety of triggers including time, location ofa user or third party, and the like. In an example, a parent may want tohave a cooking system operate to warm up ingredients based on ananticipated arrival of someone to the home. This anticipation may bebased on a detected location of a mobile device being carried by aperson whose arrival is being anticipated.

The platform 800 may further connect cooking system users withparticipants in the ecosystem (e.g., vendors and/or service providers)synergistically so that both the user and the participants may benefitfrom the platform 800. In an example, a user may plan to prepare a mealfor an upcoming dinner. The user may provide the meal plan to theplatform 800 (e.g., directly through the user's mobile phone, via theuser's intelligent cooking system 21, and the like). The platform 800may determine that fresh produce for the meal is preferred by the userand may signal to retailers and/or wholesalers to have the produceavailable for the user to pick up on his/her return to the home toprepare the meal. In this way, vendors and service providers whoparticipate in the ecosystem may gain insight into their customer'sneeds. Likewise, users may indicate a preference for a type of meal thatmay be prepared with a variety of proteins. Participants in theecosystem may make offers to the user to have one or more of the typesof protein available for the user on the day and at the time preferredby the user. A butcher that is located in proximity to the user's returnpath may offer conveniences, such as preparation of cuts of meat for theuser. Butchers who may not be conveniently located in proximity to theuser's return path may offer delivery services on a day and time thatbest complies with the user's meal plans.

A user of such a platform-connected intelligent cooking system mayleverage the platform 800 to gain both access to and analysis ofinformation that is available across the Internet to address particularuser interests, such as health, nutrition, and the like. As an example,a user may receive guidance from a health professional to reduce redmeat intake and increase his seafood intake. The platform 800 mayinteract with the user, the cooking system, and ecosystem participantsto facilitate preparing variations of a family's favorite meals withfish instead of red meat. Changes in spices, amounts, cooking times,recipes, and the like may be provided to the user and to the cookingsystem 21 through the platform 800 to make meal preparation moreenjoyable. The platform 800 may help with nutritional assistance, suchas by providing access to quality nutritional professionals who may workpersonally with a user in meal selection and preparation.

The platform 800 may also help a user of the platform 800, even one whodoes not have access to the intelligent cooking system 21, to benefitfrom the knowledge gathering and analysis possible from a platform 800interconnected with a plurality of cooking systems, users, and ecosystemparticipants. In an example, the platform 800 may provide guidance to auser in the selection and purchase of an intelligent burner and/orintegrated cooking system and related appliances (e.g., refrigeration),utensils, cookware, and the like.

The platform 800 may further facilitate integration with VAS, such asfinancial services (e.g., for financing infrastructure and operatingcosts), healthcare services (e.g., facilitating connecting healthcareproviders with patients at home), smart home solutions (e.g., thosedescribed herein), rural solutions (e.g., access to products andservices by users in rural jurisdictions), and the like. Information(e.g., profiles, analytics, and the like) gathered and/or generated bythe platform 800 may be used for other business services either directlywith or through other partners (e.g., credit rating agencies fordeveloping markets).

The platform 800 may facilitate a range of user benefits, includingshopping, infotainment, business development, and the like. In abusiness development example, a user may utilize her intelligentintegrated cooking system 21 to produce her own cooking show by settingup her personal phone with camera on the cooking system 21 so that theuser activity on the cooking system 21 may be captured and/ordistributed to other users via the platform 800. Further in the example,a user may schedule a cooking demonstration and may allow other users tocook along with him in an autonomous and/or interactive way. A user mayopt into viewing and cooking along with the cooking show producerwithout directly interacting with the producer. Whereas, another usermay configure his cooking system 21 with a personal mobile device andallow others to provide feedback based on the user's activities on thecooking system 21 via the camera and user interface of the mobiledevice.

The platform 800 may facilitate establishing an IoT ecosystem of smarthome devices, such as, in embodiments, a smart kitchen that enables andempowers the homemaker. The smart kitchen may include a smart cookingsystem 21, IoT middleware and a smart kitchen application. The smartcooking system 21 may provide a hardware layer of the platform 800 thatmay provide plug and play support for IoT devices, with each new deviceacting as a node providing more information, such as from additionalsensors, to the entire system. IoT cloud support, which may beconsidered as a middleware layer of the platform 800, may enable thecommunication (such as by streaming) and storage of data on the cloud,along with enabling optional remote management of various capabilitiesof the platform 800. A smart kitchen application may include a userinterface layer that may provide a single point of access and controlfor the entire range of smart devices for the ease of the homemaker orother users. As an example of a smart kitchen enabled by the smartcooktop methods and systems described herein, an exhaust fan may beturned on as the water in a pot begins to boil, thereby directing thesteam output of the pot away from the kitchen. This may be done througha combination of sensors (e.g., a humidity sensor), automated cookingsystem control that determines when the pot will begin to boil based onthe weight of the pot on the burner, and the energy level of the burner,and the like. Similar embodiments may be used in industrialenvironments, such as coordination with ventilation systems to maintainappropriate temperature, pressure, and humidity conditions bycoordination of heating activities via the cooking system 21 and routingand circulation of air and other fluids by the ventilation system. Thecooking system controller may, for example, communicate with an exhaustfan controller to turn on the fan based on these inputs and/orcalculations; thereby improving the operation of the smart kitchenappliances while conserving energy through timely application of theexhaust fan. A flow chart representative of operational steps 5600 forthis example is depicted in FIG. 281.

The value created by such a platform 800 may be broadly classified into(i) VAS; (ii) profiling, learning and analytics; and (iii) a smart homesolution or IoT solution for a commercial or industrial environment. TheVAS of the system may include without limitation: (a) personalizednutrition; (b) information and entertainment (also referred to as“infotainment”); (c) family health; (d) finance and commerce services(including online ordering and shopping); (e) hardware control services;and many other types of services.

Profiling, learning and analytics may provide a number of benefits tovarious entities. For example, a homemaker may get access topersonalized nutrition and fitness recommendations to improve the healthof the entire family, including healthy recipe and diet recommendations,nutritional supplement recommendations, workout and fitnessrecommendations, energy usage optimization advice for cooking and use ofother home appliances, and the like. Device manufacturers and otherenterprises may also benefit, as the platform 800 may solve the problemsfaced by home appliance device manufacturers in integrating theirdevices to the cloud and leveraging the conveniences provided by thesame. Device manufacturers and other enterprises may be provided with aninterface to the platform 800 (such as by one or more applicationprogramming interfaces, graphical user interfaces, or other interfaces)that may enable them to leverage capabilities of the platform 800,including, one or more machine learning algorithms or other analyticcapabilities that may learn and develop insights from data generated bythe device. These capabilities may include an analytics dashboard fordevices; a machine learning plug and play interface for developing datainsights; a health status check for connected appliances (e.g., to knowwhen a device turns faulty, such as to facilitate quick and easyreplacement/servicing); and user profiling capabilities, such as tofacilitate providing recommendations to users, such as based oncollaborative filtering to group users with other similar users in orderto provide targeted advice, offers, advertisements, and the like.

A smart home solution or IoT solution for a commercial or industrialenvironment may provide benefits to device manufacturers who find itdifficult to embed complex electronics in their devices to make themintelligent due to development and cost constraints. The platform 800simplifies this by providing a communication layer that may be used bypartners to send their device data, after which the platform 800 maytake over and provide meaningful data and insights by analyzing the dataand performs specific actions on behalf of an integrated smart home forthe user. Additional value of each partner interacting through theplatform 800 is the access to various sensory data built into the systemfor effectively making any connected device more intelligent. Forexample, among many possibilities, the ambient temperature sensor insidethe smart cooking system 21 may be leveraged by a controllable exhaustfacility to accordingly increase the airflow for the comfort of thehomemaker.

Referring to the smart home embodiment of FIG. 234, an intelligentcooking system 900 may be a participant in or may be a gateway to a homeappliance network that may include other kitchen appliances, sensors,monitors, user interface devices, processing devices, and the like. Thehome appliance network, and/or the devices configured in the homenetwork, may be connected to each other and to other participants of theecosystem through the platform 800 (FIG. 233). Data collected from theseappliances, participants in the ecosystem, users of the platform, thirdparties, and the like may provide an interactive environment to explore,visualize, and study patterns, such as fuel usage patterns. Datacollected may further be synthesized through deep machine learning,pattern recognition, modeling, and prediction analysis to providevaluable insights related to all aspects of the platform participants,devices, suppliers, and the larger ecosystem.

Further embodiments of the hydrogen generation and consumptioncapabilities are now described.

The system may use water and electricity as fuel to generate thegas-on-demand that may be used, for example, for cooking. The hydrogenand oxygen generated in the cell may be separated out within the celland kept separate until reaching the combustion port in a burner. Aspecially designed burner module may comprise different chambers toallow passage of hydrogen, oxygen, and cooking gas. The ports forhydrogen and cooking gas may be designed in such a way as to avoid flameflashbacks and flame lift-offs, and the like. The oxygen ports may bedesigned to ensure optimum supply of oxygen with respect to the hydrogensupply. The hydrogen and oxygen ports may be on mutually perpendicularplanes ensuring proper intermixing of the burning mixture. The hydrogenand cooking gas connections may be mutually independent and may beoperated separately or together to generate a mixed flame.

A hydrogen production and use system 1000 as disclosed herein maycomprise one or more of the following elements as depicted in FIGS. 235and 236. An electrolytic cell 1101 is detailed in FIG. 236, which showsan exploded view of the cell consisting of steel electrodes separated bynylon membranes inside polyvinyl chloride (“PVC”) gaskets sandwiched byacrylic sheets. The cell may comprise an alkaline electrolytic cell thatseparates water into its constituent components of hydrogen and oxygen.A mixture tank, such as a concentrated alkaline mixture tank may serveas the electrolyte source for the electrolytic cell. The alkali mixturemay be prepared by mixing a base like potassium hydroxide (“KOH”) orsodium hydroxide (“NaOH”) with water. In case of KOH, in embodiments theconcentration may be around 20%. The membrane for separation of gaseswithin the cell may be made from a variety of materials. One suchmaterial is a nylon sheet with catalyst coating that has enough threadcount to allow ion transfer and minimal gas transfer. The electrodesused may be, for example, stainless steel or nickel coated stainlesssteel. Also provided may be gas bubbling tanks. The hydrogen and oxygengenerated from the electrolytic cells may be passed through gas bubblingtanks. The tanks may be made with recirculation or non-recirculationmodes. In a non-recirculation mode, the gas is bubbled through water andany impurity in the gas gets purified in the process. In recirculationmode, the gas is bubbled through KOH solution, which may be identical inconcentration to the alkaline mixture tank. In this methodology, anyadditional electrolyte that flows out with the gas gets re-circulatedinto the alkaline mixture tank. The two bubbling tanks may be connectedtogether, such as at the bottom, to ensure pressure maintenance acrossthem. Dehumidifiers may also be included. The gas passed through thebubblers may have excess moisture content that reduces the combustionefficiency. Hence, the gas may be passed through dehumidifiers, whichmay use a desiccant, water-gas separator membranes, or otherdehumidification technologies, or a combination thereof, to reduce thehumidity content of the gas. A hydrogen burner arrangement is providedwherein a conventional hydrogen burner, as known in the art, may beconnected to the dehumidifier, such as through a flashback arrestor. Inembodiments, there are no ports for air intake, as combustion of thehydrogen-air mixture may result in an elevated concentration ofmono-nitrogen oxides (“NOx”), which in turn may result in a potentialfor flame flashback. The burner ports may have a small diameter, such aslower than 0.5 mm, to reduce the chance of any flame flashback. Theports may be aligned in such a way as to cross-ignite, resulting incombustion of the complete gas supply with a single spark. The hydrogenconcentration throughout the supply line may be above the maximumcombustion limit, and hence there is little safety hazard. The oxygensupply may be through a channel that is completely separate from thehydrogen one. The oxygen ports may be located on a plane perpendicularto the hydrogen ports to ensure proper mixing of the combustion mixture.Above the burner, a catalyst may be placed so as to lower thetemperature of combustion, reducing the concentration of NOx generated.An economically feasible high temperature catalyst mesh may be used tolower the temperatures of combustion.

The power supply may supply a desired voltage that may be optimizedaccording to the conditions of the system, such as the watertemperature, pressure, etc. The voltage per cell may vary, such as from1.4 v to 2.3 v, and the current density may be as low as 44 mA/cm² formaximum efficiency. As the current density is low, the efficiency tendsto be high.

An LPG/cooking gas burner arrangement may be provided. The LPG/cookinggas burner arrangement may be added to the hydrogen burner arrangement.In embodiments, the system may be similar to a closed top burnerarrangement, where the burner ports are along the sides of the burnerand the flame fueled by the LPG surrounds the hydrogen flame. Inembodiments, the gas supply channel may be kept separate from thehydrogen supply channel and the oxygen supply channel and would hencepose no safety risk in that regard. In alternative embodiments, thefuels may be mixed, such as under control of a processor.

A renewable energy connection may be provided. In embodiments, the wholesystem, including the storage system, may be connected to renewableenergy sources like solar power, wind power, water power, or the like.The hydrogen storage may act as storage for energy produced by such arenewable energy source.

In yet another embodiment of the system, the actuation of the combustionmay be done using a sensor placed along the oxygen supply channel todetect the presence of a cooking utensil on the burner. The sensor maybe shielded from the heat and made to work at an optimum temperature.

In yet another embodiment of the system, the hydrogen flame may be usedto heat a coil that could hence radiate heat for more spread outcooking. The hydrogen supply to the radiator may be regulated by thetemperature within the radiator.

In yet another embodiment of the system, the heat absorbed by thecatalyst mesh may be used to generate electric power, increasing the netefficiency of the system.

The hydrogen production system may be integrated into a cooking system1201 as depicted in FIG. 237, which may include smart cooking systemcomprising a microcontroller with basic sensors, such as gyro,accelerometer, temperature and humidity. Other sensors like weight,additional temperature sensors, pressure sensors, and the like may bemounted on the cooking system and, based upon various inputs from theuser and the system (including optional remote control), the actuatorsmay control the cooking temperature, time and other cooking functions.

A speaker may sometimes be used to read out the output or simply playmusic.

The microcontroller may also be interfaced with a display and touchinterface.

The microcontroller may be connected with the cloud, where informationregarding recipes, weight and temperature, and the like may be storedand accessed by the controller. The microcontroller may also provideinformation on the user's cooking patterns.

In an embodiment, smart system configuration, control, and cookingalgorithms may be executed by computers (e.g., in the cloud) to processall gathered and sensed information, optionally providing arecommendation related to the operation to the end user. Therecommendation may include suggesting suitable recipes, auto turning ofthe heat in the burner, and the like. The microcontroller maycommunicate via Bluetooth low energy (“BLE”), Wi-Fi and/or lowaran, orthe like, such as to ensure connectivity to the cloud. Lowaran is awireless network that leverages long-range radio signals forcommunicating between IoT devices and cloud devices via a centralserver. The microcontroller may be designed in such a way that it hasenough processing power to connect to other IoT devices that may havelittle or no processing power and also do processing for these IoTdevices to give the end user a smart and intelligent, all in one, smarthome solution.

FIG. 238 and FIG. 239 depict auto-switching connectivity 1301 in theform of ad hoc Wi-Fi from a cooktop 1310 through nearby mobile devices1371 may be performed in the event of non-availability of a common homeWi-Fi router 1340 to ensure cloud connectivity 1360 whenever possible.FIG. 238 depicts a normal connectivity mode when the Wi-Fi router 1340is available. FIG. 239 depicts ad hoc use of local mobile devices 1400for connectivity to the cloud 1360.

Additional smart cooking system features and capabilities may includeweight sensors for each heating element that, when combined with cookinglearning algorithms, may control fuel consumption to minimizeovercooking and waste of fuel. This may also benefit configurations thatemploy multiple heating elements, so that unused heating elements do notcontinue to operate and waste fuel. FIG. 240 depicts a three-elementinduction smart cooking system 1500. Heating elements may be gas-basedor may alternatively include heating with induction, electric hot plate,electric coil, halogen lamp, and the like. FIG. 241 depicts a singleburner gas smart cooking system 1600. FIG. 242 depicts an electric hotplate (coil) smart cooking system 1700. FIG. 243 depicts a singleinduction heating element smart cooking system 1800.

Another embodiment of smart cooking technology described herein may bean intelligent, computerized knob, dial, slider, or the like suitablefor direct use with any of the cooktops, probes, single burner elements,and the like described herein. Such a smart knob 2000 may include allelectronics and power necessary for independent operation and control ofthe smart systems described herein. References to a smart knob 2000should be understood to encompass knobs, dials, sliders, toggles andother physical user interface form factors that are conventionally usedto control temperature, timing and other factors involved in heating,cooking, and the like, where any of the foregoing are embodied with aprocessor and one or more other intelligent features.

The smart knob 2000 may include an embodiment with a digital actuator,such as for electric-based cooking systems and another embodiment with amechanical actuator, such as for gas models. The smart knob 2000 may bedesigned with portability and functionality in mind. The knob mayinclude a user interface (e.g., display, audio output, and the like)through which it may provide users step-by-step recipes, and the like.The smart knob 2000 may operate wirelessly, so that it may set alarmsand also monitor the operation of a plurality of smart cooking systems21 even if it is removed from the cooking system actuator. The smartknob 2000 may, in embodiments, store information that allows it tointerface with different kinds of cooking systems, such as by includingprograms and instructions for forming a handshake (e.g., by Bluetooth™or the like) with a cooking system to determine what control protocolshould be used for the cooking system, such as one that may be managedremotely, such as in a cloud or other distributed computing platform. Inembodiments, a user may bring the smart knob 2000 in proximity to thecooking system 21, in which case a handshake may be initiated (eitherunder user control or automatically), such that the smart knob 2000 mayrecognize the cooking system 21 and either initiate control based onstored instructions on the smart knob 2000 or initiate a download ofappropriate programming and control instructions for the cooking system21 from a remote source, such as a cloud or other distributed computingplatform to which the smart knob 2000 is connected. Thus, the smart knob2000 serves as a universal remote controller for a variety of cookingsystems, where a user may initiate control using familiar motions, suchas turning a dial to set a timer or temperature setting, moving a toggleor slider up or down, setting a timer, or the like. In embodiments, aplurality of smart knobs 2000 may be provided that coordinate with eachother to control a single burner or heating element or a collection ofburners or heating elements. For example, one of the smart knobs 2000 ina pair of knobs might control temperature of a burner or heatingelement, while a second knob in the pair might control timing for theheating.

In embodiments, the smart knob 2000 may be used to embody complexprotocols, such as patterns of temperatures over time, such as suitablefor heating an item to different temperatures over time. These may bestored as recipes, or the like, so that a user may simply indicate, viathe smart knob 2000, the desired recipe, and the smart knob 2000 willautomatically initiate control of a burner or heating element to followthe recipe.

A user may use the smart knob 2000 with an induction cooking system forcontrolling the temperature of a cooking system, such as an inductionstove, providing step-by-step instructions, and the like. The user may,for example, switch to cooking with a gas burner-based smart cookingsystem by simply taking the smart knob 2000 off of the induction cookingsystem, configuring it to operate the gas burner cooking system (such asby initiating an automated handshake), and mounting the smart knob 2000in a convenient place, such as a countertop, wall, refrigerator door,and the like. It should be noted that while the smart knob 2000 may beplaced on the cooking system, once a connection has been established,such as by Bluetooth™, near-field communication (“NFC”), Wi-Fi, or byprogramming, the smart knob 2000 may be placed at any convenientlocation, such as on the person of a user (such as where a user ismoving from place to place in an industrial environment), on a dashboardor other control system that controls multiple devices, or on anotherobject. The smart knob 2000 may be provided with alternative interfacesfor being disposed, such as clips for attachment to objects,hook-and-loop fasteners, magnetic fasteners, and physical connectors.

The smart knob 2000 may use, include or control the various features ofthe smart cooking systems 21 described throughout this disclosure.Additionally, the smart knob 2000 may be connected to other IoT devices,such as smart doorbell, remote temperature probe (e.g., in arefrigerator or freezer), and the like. The smart knob 2000 may be usedfor kitchen tasks other than cooking. By connecting with a temperatureprobe, the smart knob 2000 may be used to inform a user of the progressof an item placed in the refrigerator or freezer to cool down.

As it requires only very little power and as it is mountable on thesmart cooking system 21, the smart knob 2000 may, in embodiments, berecharged through thermoelectric conversion of the heat from a burner onthe cooking system 21, so that the use of external power supply is notrequired.

FIGS. 244-251 depict a variety of user interface features 2010, 2020,2101, 2201, 2300, 2400, 2500, 2600 of the smart knob 2000.

FIG. 252 depicts a smart knob 2700 deployed on a single heating elementcooking system 2710, while FIG. 253 depicts a smart knob 2800 placed ona side of a kitchen appliance 2810.

Other features of a smart cooking system 21 may include examples ofsmart temperature probes 3101 depicted in FIGS. 254-257. The temperatureprobe 3101 may consist of a wired or wireless temperature sensor thatmay be interfaced with a smart cooking system 21, the smart knob 2000,and/or a mobile phone 150 for cooking. The temperature probe 3101 may,in embodiments, be dipped into a liquid (such as a soup, etc.) orinserted interior of a solid (such as a piece of meat or a cooking bakedgood), to cook very precisely based on the measured interior temperatureof the liquid or solid. Also, the smart temperature probe 3101 mayfacilitate use of an induction base to control the temperature of thebase for heating water to a precise temperature (e.g., for tea) with anytype of non-magnetic cooking vessel.

The smart cooking system 21 may include a smart phone docking station3301 that may be configured to prevent cooking heat from directlyimpacting a device in the station while facilitating easy access to thephone for docking, undocking and viewing. A variety of different docks3310, 3401, 3501, 3601, 3701, 3801 for compatibility with a range ofsmart phone and tablet devices are depicted in FIGS. 258-263.

Various burner designs are contemplated for use with a smart cookingsystem as described herein. FIGS. 264-280 depict exemplary burners 3900,4200, 4701, 5000, 5300.

The Internet-connected smart cooking system 21 described herein mayinclude tools and features that may help a user, such as a homemaker, acommercial chef, or cook in an industrial environment to preparehealthier meals, learn about food choices of other users, facilitatereduced meal preparation time, and repeatable cooking for improvedquality and value. A few applications that may leverage the capabilitiesof the present Internet-connected smart cooktop may include a fitnessapplication that helps one estimate daily calorie consumptionrequirements for each member of a user's family or other person for whomthe user may prepare meals. This may help a user to control and trackthe user's family fitness over time. Using data from recipes and weightsensors for pots/pans used to cook the food for the recipes, a fitnessapplication may generate a calorie consumption estimate and suggest oneor more healthy alternative recipes. Through combining sensing andcontrol of the cooktop functionality (e.g., burners) with Internetaccess to food nutrition and weight values for recipe ingredients beingcooked, the calorie count of a content of a pan placed on a smartcooktop burner may be estimated. As an example, if a recipe calls for ¼cup of lentils per serving combined with a serving-unit of water, atotal weight of a pan being used to prepare the lentils may be sensed.By knowing the weight of the pan, a net weight of the ingredients in thepan may be calculated so that a number of servings in the pan may bedetermined by calculating the total weight and dividing it by a weightper serving. By accessing recipe comparison tools (e.g., as may beavailable via resources on the Internet) that may include lists ofcorresponding meals that have lower fat, higher nutritional ingredients,alternate recipes could be suggested to the user that would providecomparable nutrition with lower calories or fat, for example.

A food investigation application may gather information from the smartcooktops and user activity about recipes being used by users of thesmart cooktop systems throughout a region (e.g., a country such asIndia) to calculate various metrics, such as most often cooked recipe,preferred breakfast meal, popular holiday recipes, and the like. Thisinformation may be useful in planning purposes by food suppliers,farmers, homeowners, and the like. As an example, on any given day,information about the recipes that people in your region are preparingmight be useful in determining which dishes are trending. AnInternet-based server that receives recipe and corresponding limiteddemographic information over time may determine which meals aretrending. A count of all uses of all recipes (or comparable recipes)during a period of time (e.g., during evening meal preparation time) maybe calculated and the recipes with the greatest use counts could beidentified as most popular, currently trending, and the like.

Cooking becomes more repeatable so a cook (e.g., a less experiencedcook) may rely on the automation capabilities of an Internet-connectedsmart cooktop system to avoid mistakes, like overcooking, burning due toexcessive heat, and the like. This may be possible due to use ofinformation about the items being cooked and the cooking environment,such as the caloric output value of each burner in any heat outputsetting, the weight of the food being cooked, target temperature andcooking time (e.g., from a recipe), a selected doneness of the food, andthe like. By combining this information with modeled and/or sensedburner operation (e.g., temperature probes may be used to detect thetemperature of the food being cooked, the temperature of the cookingenvironment, and the like) to facilitate automated control of heat,temperature, and cooking time thereby making meal cooking repeatable andpredictable. Each type of burner (e.g., induction, electric, LP gas,hydrogen gas, and the like) may each be fully modeled for operationalfactors so that cooking a recipe with induction heating today and withhydrogen gas heat tomorrow will produce repeatable results. Similarcapabilities to combine information from the cooking system andinformation from sensors or other systems may be used to improverepeatability and improvement of industrial processes, such asmanufacturing processes that produce materials and components throughheating, drying, curing, and the like.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with hydrogen production, storage, and usesystems. In embodiments, the hydrogen production, storage, and usesystems may use renewable energy as a source of energy for variousoperations including hydrogen production, hydrogen storage,distribution, monitoring, consumption and the like. In embodiments,hydrogen production, such as with a hydrolyzer system, may be powered byrenewable energy such as solar power (including systems using directsolar power) and photovoltaic systems (including ones usingsemiconductors, polymers, and other forms of photovoltaic), hydro power(including wave motion, running water, or stored potential energy),gravity (such as involving stored potential energy), geothermal energy,energy derived from a thermal gradient (such as a temperature gradientin a body of water, such as ocean water, or a temperature gradientbetween a level of the earth, such as the surface, and another level,such as a subterranean area), wind power and the like and whereapplicable. References to renewable energy throughout this disclosureshould be understood to encompass any of the above except where thecontext indicates otherwise.

In embodiments, solar collector panels or the like may be configuredwith a hydrogen production system, such as a system described herein, toprovide electricity for powering the production of hydrogen, includingfrom water. A hydrogen production system may be built with integratedsolar collector panels and the ability to connect to further solarsystems, so that placement of the hydrogen production system in anambient environment that is exposed to sunlight may facilitate itsself-powered operation or partially-self-powered operation via solarpower.

In embodiments, solar power harvesting subsystems, such as a singlepanel or an array of solar panels, may be configured to be deployedseparately, and optionally remotely, from the hydrogen productionsystem. Solar power harvesting subsystems may be connected to one ormore hydrogen production systems to facilitate deployment inenvironments with localized limited access to sunlight, such as in amulti-unit dwelling, a building with few windows, a building withinterior areas that do not receive direct or sufficient sunlight (suchas a warehouse, manufacturing facility, storage facility, laboratory, orthe like) and the like. Other operational processes of a system forhydrogen production, storage, and use may be powered via solar power.

Solar energy harvested for the production of hydrogen may be sharedand/or diverted to these other operations or sold back into the localgrid as needed. Solar energy harvesting may also be used to charge abattery, charge various thermal systems, or other electrical energystorage facility that may directly provide the energy needed forhydrogen production immediately or with a time-shift and on-demandfunctions and other operational elements as described herein. In thisway, while solar power provides a renewable source of energy, the impactof an absence of sunlight and therefore diminished solar powerproduction may be mitigated through the use of an intermediate batteryor the like.

In embodiments, a data collection system, involving one or more sensorsand instruments, may be used to monitor the solar power system orcomponents thereof, including to enable predictive maintenance, toenable optimal operation (including based on current and anticipatedstate information), and the like. Monitoring, remote control, andautonomous control may be enabled using machine learning and artificialintelligence, optionally under human training or supervision, as withother embodiments described herein. These capabilities for datacollection, monitoring, and control, including using machine learning,may be used in connection with the other renewable energy systems, andcomponents thereof, described throughout this disclosure.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with other sources of renewable energyincluding wind power. Wind power may be harvested through a windmill,turbine, roots-blade configuration, or similar wind power collectionfacility that may be configured with the hydrogen production, storageand use systems and components similar to a solar collection facility orother electric sources as described herein. In many examples,configuring a turbine or similar wind power collection and conversiondevice attached to a hydrogen production, storage, and use system mayfacilitate deployment in a variety of environments where sufficientmoving gas (such as blowing wind, air flowing around a moving element(such as part of a vehicle), exhaust from an industrial machine orprocess, or the like) is available. These and other embodiments areintended to be encompassed by the term “air flow” in this disclosureexcept where the context indicates otherwise.

In embodiments, a variety of sources of air movement may be utilized asa source of power from the air flow. In various examples, heated airthat may result from the use of the hydrogen, such as for cooking andthe like, may pass through a wind harvesting facility, such as a turbinethat may be disposed in the heated air flow path. In embodiments, otherheat harvesting devices may be deployed such as positive displacementdevice or other heated mediums through which energy may be absorbed andpower a suitable heat engine. In embodiments, disposing a turbine orother energy/heat harvesting devices directly above a stove, cookingsystem, or other heat generating use of the hydrogen produced mayproduce energy that may be used to power, directly or indirectly,partially or wholly, such as through recharging a battery, operationalprocesses of a hydrogen production, storage and use system.

In yet another use of renewable energy for powering one or moreoperational processes of a hydrogen production, storage, and/or usesystem, such as may be described herein, hydropower may be a source ofrenewable energy. In embodiments, hydropower may be converted into aform that is usable to operate processes of a hydrogen production,storage and use system as described herein including electricalproduction and possibly harvesting mechanical power. In these examples,electricity from hydropower may be utilized to operate a hydrolyzer toproduce hydrogen from a hydrogen source, such as water or ambientair-based water vapor. In embodiments, configuring a hydrogenproduction, storage, and use system that may directly utilize hydropower may involve building an enclosure that keeps a source ofhydropower, such as a moving body of water (e.g., a river, waterfall,water flowing through a dam, and the like) from interfering with theoperational processes such as hydrogen production, storage, and use. Inembodiments, such an enclosure may facilitate deployment of ahydropower-sourced system directly in a flow of water by making at leastportions of such a system submersible. Hydrogen production and storage,for example, may benefit from such an enclosure. In particular, asubmersible hydrogen production system may take advantage of thehydrodynamic water in which the system is submerged as a source ofhydrogen, as a source of energy to produce the hydrogen, as a source tocool the process, or the like.

Referring to FIG. 282, embodiments of the methods and systems related torenewable energy sources for hydrogen production, storage, distributionand use are depicted. A system that facilitates use of renewable energyas described herein may include a hydrogen production facility 5074 thatmay be coupled to a hydrogen storage facility 5703. A hydrogenproduction facility 5705 and/or the hydrogen storage facility 5703 maybe coupled to one or more hydrogen use facilities 5707. One or more ofthe hydrogen use facilities 5707 may be coupled through a hydrogendistribution network (not shown).

Hydrogen production, storage, distribution, and use may be at leastpartially powered by one or more renewable energy sources, such as asolar energy source 5709, a wind energy source 5711, a hydro energysource 5713, a geothermal energy source 5715, and the like. The windenergy source 5711 may be natural air currents, motor driven aircurrents, air currents resulting from movement of a vehicle, or wasteair flow sources 5719 (such as waste heat from heating operations, suchas cooking and the like). Any of these renewable energy sources may beconverted into a form of energy that is suitable for an intended use bythe hydrogen production, storage, distribution, and use system. As anexample, a solar energy source 5709 may be converted to electricity asdescribed herein to provide electrical power to the hydrogen productionfacility 5705, the hydrogen storage facility 5703, the hydrogen usefacility 5707 and the like. It will be appreciated in light of thedisclosure that the hydrogen storage facility 5703 need not be requiredto operate with the hydrogen production facility 5705 and the hydrogenuse facility 5707 as the produced hydrogen may be consumed upon itsproduction without a need for storage.

Another form of energy that may be sourced by the hydrogen productionfacility 5705 may include a sulfur dioxide source 5717, such as fossilfuel combustion systems that produce waste sulfur dioxide. As describedherein, the sulfur dioxide source 5717 may supply heat energy and rawmaterial from which hydrogen gas may be produced by the hydrogenproduction facility 5705 adapted to use sulfur dioxide.

Yet another form of energy that may be sourced by the hydrogenproduction facility 5705 and/or the hydrogen storage facility 5703 mayinclude heat recapture 5721 from one or more of the hydrogen usefacilities 5705. The recovered heat may be used directly, converted intoanother form, such as steam and/or electricity, or provided as input rawmaterial from which hydrogen may be harvested.

Referring to FIG. 283, an alternate embodiment of renewable energy usewith at least one hydrogen production facility 5705, at least onehydrogen storage facility 5703. In the embodiment of FIG. 283, hydrogenproduction, storage, distribution, and uses may be connected, but maynot be integrated, such as into a standalone combined function system.In the embodiment of FIG. 283, renewable energy sources as described forthe embodiment of FIG. 282 may be used to provide energy for thehydrogen production 5705 and storage 5703. However, hydrogen use may beprovided through a hydrogen distribution system 5823 that may be coupledto the hydrogen production facility 5705, storage facility 5703 and tothe hydrogen use facilities 5707 that may be located at distinctphysical locations, such as individual apartments in an apartmentbuilding, and the like.

Referring to FIG. 284, the methods and systems described herein forhydrogen production, storage, distribution, use, and control may becoupled with predictive maintenance methods and systems to facilitateimprovements in operation with less unplanned downtime and fewercomponent failures. In the embodiment of FIG. 284, a predictivemaintenance facility 5903 may be configured to operate on a processorassociated with or more particularly integrated with a hydrogenproduction, storage, and use facility. Alternatively, predictivemaintenance facility may be configured to operate on a processor that isnot integrated, such as a cloud computer, a standalone computer, anetworked server, and the like. The predictive maintenance facility 5903may receive input from various system sensors 5905 along withinformation from various data sets, such as a use/maintenance model5915, a warranty and standards rules 5919, and an archive of sensor dataand an analytics derived there from 5917, among other sources.

The system sensors 5905 may include hydrogen system sensors, inputenergy sensors, process sensors (e.g., catalytic sensors and the like),output sensors, use sensors, and a range of other sensors as describedherein. Each or any of these sensors may provide data directly orthrough an intermediate processor a data acquisition unit, across-linked data acquisition unit, and the like to the predictivemaintenance facility 5903. For a local/integrated predictive maintenancefacility 5903, sensor data may be provided through a range of inputs,including direct inputs and the like. For a remote/cloud preventivemaintenance facility, sensor data may be provided through a networkinginterface, such as the Internet, an intranet, a wireless communicationchannel, and the like.

The predictive maintenance facility 5903 may further be coupled with alocal or remote user interface for providing reports, facilitatingcontrol, interacting with the predictive maintenance facility 5903 tofacilitate user participation in maintenance actions, planning, andanalysis. A user interface facility 5909 may be integrated with thepredictive maintenance facility 5903, such as being an integratedcomponent of a hydrogen production, storage, and use system.Alternatively, the user interface 5909 may be remotely accessible, suchas through a network, a cloud network facility, and the like includingwithout limitation the Internet and the like.

To facilitate at least semi-automated predictive maintenance,replacement parts, service, and the like may be automatically orderedbased on a result of the predictive maintenance facility 5903 indicatingthat some form of preventive activity is required. An automaticpart/service ordering facility 5913 may be connected directly orindirectly to the user interface/control facility 5909 to enable usersto approve or adjust an automated order.

The embodiments of FIG. 284 include at least two configurations; (i) anintegrated hydrogen cooking/heating system with predictive maintenance5911, and (ii) modular system that may take advantage of sharedresources such as cloud computing capabilities, cloud storage facilitiesand the like.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with one or more computing devicefunctions that interface with operational, monitoring, and otherelectronic aspects of a hydrogen production, storage and optional usesystem as described herein and that may be accessed through a variety ofinterfaces. Functions, several of which are described elsewhere herein,may include control and monitoring of hydrogen production, control, andmonitoring of hydrogen storage including distribution and the like,control and monitoring of the use of generated and/or stored hydrogen.In embodiments, access to these functions, such as to provide controlinput and receive monitor output, may be done through an interface, suchas an application programming interface (API) or an interface to one ormore services, such as in a services oriented architecture, that mayexpose certain aspects of these functions, services, components, or thelike, to facilitate access thereto. The terms “API” or “applicationprogramming interface” should be understood to encompass a variety ofsuch interfaces to programs, services, components, computing elements,and the like except where the context indicates otherwise.

In embodiments, API type interfaces may include a library of features,such as algorithms, software routines, and the like through which theexposed aspects may be accessed. In embodiments, API type interfaces mayfacilitate access to a control function of a hydrogen productionsubsystem as described herein to enable third-party control and/ormonitoring of the subsystem, to facilitate analytics with outsideresources, to facilitate interconnection of multiple resources,coordination of fuel and renewables between multiple systems, and thelike. In embodiments, a single hydrogen production subsystem may beutilized to provide hydrogen to a plurality of hydrogen storage systems.By way of these examples, one or more of the hydrogen storage systemsmay use the API or API-type interface to access a flow valve, fueldistribution architecture, or the like that may facilitate distributionof hydrogen produced by the storage systems so that storage systems thatare at or near storage capacity may direct a control function of theflow valve to reduce or stop distribution of the hydrogen to the storagesystem. In embodiments, Application programming interfaces may beutilized across a range of control and monitoring functions, includingproviding access to hydrogen consumption monitoring elements, renewableenergy utilization monitoring systems, hydrogen use systems, smartcooktop systems as described herein, and the like.

In addition to API type interfaces as described herein, a hydrogenproduction, storage, and use system may be accessed through one or moremachine-to-machine interfaces. In embodiments, such interfaces mayinclude directly wired interfaces, such as between a monitoring machineand a sensor disposed to sense the flow of water, the flow of energyused for hydrolysis, the flow of resulting hydrogen, or one or morelevels, such as liquid levels, of any of the foregoing. In embodiments,machine-to-machine interfaces may be indirect, such as through astandard communication portal such as network, e.g., an intranet, anextranet, the Internet, and the like. In embodiments, communicationprotocols such as HTTP and the like may be utilized to exchange control,monitoring, and other information between some portion of the hydrogenproduction, storage, and use system and another machine. In embodiments,a machine-to-machine interface may facilitate third party control ofhydrogen use. This may manifest itself in a variety of modes, examplesof which may be a user remotely accessing a cooking function from hismobile device using the Internet as a machine-to-machine interfacebetween the mobile device and the cooking function.

In embodiments, interfacing with a hydrogen production, storage and usesystem as described herein may also be accomplished through a graphicaluser interface (GUI). In the many examples, such an interface mayfacilitate human direct access to control, monitoring, and otherfeatures of the system. In embodiments, a GUI may include a variety ofscreens that may be logically related to facilitating user access to arange of features of the system within a single GUI. In the manyexamples, there may be a main system GUI screen that may include linksto a main production GUI screen that may include, among other things,links to further production GUI screens, e.g., a main screen may link toan energy source control screen, a storage system control, systemhealth, predictive information, and the like. In embodiments, a main GUIscreen may also facilitate accessing one or more GUI screens for otheraspects of the system, such as hydrogen storage monitoring and control,hydrogen distribution monitoring and control, hydrogen use, cookingfunctions of a smart cooktop, heating functions for a heater subsystem,and the like.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with predictive maintenance functions thatmay facilitate smart replacement of components thereby avoiding failureand down time. In embodiments, predictive maintenance functions that aredescribed herein may be further enhanced using one or more sensors thatmay facilitate monitoring and/or control of portions of the system thatmay require maintenance. In the examples, one or more sensors may bedeployed that facilitate monitoring and/or control of an electrolyzerfunction. By way of the examples, the one or more sensors that maymonitor the membrane portion of the electrolyzer may provide data thatmay be useful for detecting one or more conditions that requiresattention immediately or may culminate with other factors and may laterrequire attention, such as a condition that requires the membrane to bereplaced. Such sensors may further be configured to generate one or morealerts, such as audio, visual, electronic, logical signals when sensinga condition that may indicate replacement of the membrane or otherportion of the hydrolyzer is recommended. Such sensors may further beconfigured to generate one or more alerts that may trigger one or morerecordings of data from the sensors for a long duration to capturesignals that may capture events at various intervals, frequencies, andmagnitudes that may be indicative of the need to replace the membrane orother portion of the hydrolyzer. Examples of the membrane and theelectrolyzer are disclosed in U.S. Pat. No. 8,057,646 to Hioatsu, et al,filed on 7 Dec. 2005, and U.S. Pat. No. 6,554,978 to Vandenborre, filed1 Jun. 2001, each of which is hereby incorporated by reference as iffully set forth herein.

In embodiments, such alerts may be generated by the sensors and/or byone or more computing facilities that may interface with the sensors andmay analyze data from the sensors. In embodiments, sensors, such as amembrane sensor, may be integrated into the system physically (tomonitor a physical aspect of the system), and/or logically (such as analgorithm that processes data from one or more sensors). In embodiments,one or more membrane sensors, or the like, may detect one or moreconditions that may be indicative that another action or precautionshould be taken. In embodiments, one or more alerts from such sensorsmay indicate the type of condition sensed as well as a degree of thecondition sensed. In embodiments, when sensor alert and/or sensor datais combined with other information known about the system, an alert maybe generated that indicates one or more actions or precautions thatshould be taken to counteract the condition causing the alert. In oneexample, an alert (or set of alerts) may require an action to reduce anamount of hydrogen being produced, such as by turning off or cyclingwith a greater duty cycle the operation of the hydrolyzer.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with sensors that may monitorinterconnections for corrosion or other conditions, such as internalbuildup that reduces the flow of hydrogen or the like through theinterconnections that may be associated with the system. In embodiments,such sensors may provide data indicative of a degree of corrosion,conditions that might speed corrosion, and the like to a computingdevice that may detect a condition indicative of needing to take actionimmediately or at such time as the degree of corrosion would demand suchas replace an affected portion of the interconnections. In an example,the one or more conditions may be determined by comparing data from theone or more sensors with data values that suggest an unacceptable degreeof corrosion.

In embodiments, a monitoring subsystem with one or more sensors maycollect, analyze, and/or report the real-time measurement of senseddata. Likewise, such a subsystem may collect, analyze, and/or reportreal-time failure data, such as to facilitate measuring and/or trackingmaterial failure data, e.g., frequency, degree, time since deployment,and the like.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with other sensing modalities to monitorcatalytic activities to determine, for example, catalytic performance,efficiencies and the like. Based on these sensed activities, alerts thatmay indicate a need for catalyst replacement and/or other actions orprecautions to be performed may be generated.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with various methods and systems tomonitor and determine input demand, output production, need forincreases therein, and the like.

In embodiments, a facility with multiple hydrogen operations includingproduction and/or storage may be shown to benefit from monitoring tobalance storage and production rate capacity, such as for variabledemand. In embodiments, monitoring input demand may provide insight intothe amount of hydrogen being used, when it is used, with what othergases it is being used, which use subsystems are demanding input,quality of hydrogen produced, amount of energy required to produce thehydrogen, rate of hydrogen production and use over time and under avariety of conditions, and the like. In embodiments, sensors may bedeployed and integrated with monitoring and control systems to monitorand coordinate efficient and safe storage or transfer of hydrogen.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with one or more sensors to monitor andcoordinate efficient and safe storage and/or transfer of hydrogen may beimplemented in the IoT applications. In examples when hydrogen is storedas part of a micro/smart grid solution, monitoring system functions,such as input demand, production, and storage may facilitate determininga need for increasing input/supply. Likewise, sources of energy foroperating a hydrolyzer and the like as described herein, such asrenewable energy from solar and wind may be managed so that availablesunlight and/or the wind may be tied to hydrogen production demandpredictions from users such as industrial and others. In embodiments,this may facilitate ensuring allocation of available hydrogen for gridstability and the like. In embodiments, sensors that measure integratedenergy use may similarly provide information to further facilitatemanaging for grid stability, among other things. In examples, predicteddemand may be used in determining when and how much hydrogen should beproduced and whether it should be stored to facilitate grid stability.In embodiments, this information may be used when portions of a grid arepredicted to have high demand, while other portions are predicted tohave low demand. Supply, from the production of hydrogen and/or fromstored hydrogen, may be directed where it is predicted to be needed orit is predicted to be needed in possibly relatively fewer quantities butmay be consumed more quickly.

In embodiments, another form of system sensing may involve fuel qualitysensing. In embodiments, sensors that may accurately measure fuel andoxidant compositional characteristics may be used in a control system todirect hydrogen to different storage facilities based on theinformation. By way of these examples, uses of hydrogen that maytolerate higher oxidant composition may be sourced from storagefacilities appropriately, perhaps at a lower cost than for hydrogen witha lower oxidant composition.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with sufficiently reliable flamemonitoring systems that may sense one or more of flame quality, flamestability, flame temperature, and the like. In embodiments, the methodsand systems disclosed herein may include, connect with or be integratedwith one or more sensors that may provide for continuous flue gasanalysis that may be used to adjust the efficiency and magnitude of theflame. In embodiments, further sensors and control systems related toflame or combustion products monitoring may be used including one ormore continuous heat flux meters.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with one or more particle sensors todetermine how clean something is, e.g., exhaust and/or ambient releasefrom a process or liquid including from hydrocarbon combustion. Inembodiments, one or more emission detection sensors may be used detectinefficient combustion and may also be used to detect leaks from thesystem. By way of these examples, the one or more sensors may beconfigured to measure partial pressure or particle count when sensinginternal and/or external emission such as diatomic hydrogen, carbondioxide, carbon monoxide, and other combustion byproducts. The one ormore sensors may be configured to measure combustion wave front,cylinder head temperature, lubrication cleanliness and/or entrainment,various vibration signals that may be indicative improper operation.

In embodiments, methods and systems that may include, connect with, orbe integrated with hydrogen production, storage, and use may be deployedin a variety of environments. Systems that may facilitate production ofa consumable energy source, such as hydrogen gas may be utilized inenvironments such as cooking meals or food preparation heating and/orcooking processes, including without limitation industrial cooking.

Preparation of meals or of food items that may be stored long term, suchas canned foods and the like may be performed with the methods andsystems described herein. Preparation of meals or food items inenvironments in which direct access to a reliable source of energy, suchas electricity, natural gas, or other household combustibles for cookingor otherwise is not readily available, such as in mobile, sea-borne,air-borne, and other environments that are often actively in travel maybe shown to benefit from the methods and systems described herein forautonomous production of hydrogen gas for use as a cooking energysource. Use of a cooking system that is described herein may bebeneficial for use in mobile environments by reducing a total amount offuel to be stored for use while in motion. By producing a clean burningenergy source, such as hydrogen from renewable energy sources andthrough harvesting hydrogen from an ambient environment, deploying suchsystems on long duration travel vehicles, such as cargo ships, militaryships, submarines, and the like may reduce the payload required to becarried for purposes such as meal preparation, cooking and the like.

Renewable energy to power processes of hydrogen production, monitoring,storage, distribution, and use may be harvested through the methods andsystems described herein including solar power harvesting, wind powerharvesting, thermal (e.g., geothermal) when deployed in mobileenvironments. Solar energy harvesting systems or components thereof thatmay be included with, connected to, or integrated with the hydrogenproduction, storage and use systems described herein may be deployed onsun-exposed surfaces, such as a roof of a vehicle, aircraft, ship, andthe like. Air movement around and/or through a moving vehicle, as aresult of propulsion of the vehicle and the like may be harvested andconverted into an energy source suitable for use with hydrogenproduction, storage, distribution and the like. Heat generated by mobilesystem propulsion systems may be converted into a form of energysuitable for use in production, storage, distribution, and use ofhydrogen. This may be accomplished through the use of inline turbinesystems, other heat and energy extraction machines, wind capturesystems, exhaust heat recapture systems, and the like. By using thesereadily available sources of energy, many of which are not otherwiseutilized, total external energy requirements that may only be metthrough onboard storage, may be significantly reduced.

Use of the methods and systems for hydrogen storage and use may includedeployment in marine transportation, such as on a submarine where thegeneration of toxic waste gas is undesirable. Hydrogen gas may beproduced from sea water, stored as needed onboard, and safely consumedfor cooking and other heating uses in a submarine without risk or costsof dealing with waste gas cleansing or removal. The hydrogen gas may beproduced from sea water but not stored any only generated and consumedas needed onboard, and safely consumed for cooking and other heatinguses in a submarine.

Other environments of deployment of the hydrogen-based systems describedherein may include use on aircraft, such as for preparation of meals tobe consumed on the flight. Other aircraft-based uses may includeindustrial cooking while in-flight to, for example, produce cooked goodsfor use, storage or distribution after the aircraft returns to earth.Inflight-based cooking with the methods and systems for autonomoushydrogen cooking systems and the like described herein may facilitatecooking food and the like for extended duration flights, such asaircraft that remains aloft rather than just being operated from onelocation to another. Meals, foods, and other goods could be cooked whilein-flight may be transported to/from the in-flight aircraft throughshuttle or other aircraft to facilitate longer duration flights.

Earth-bound operations such as drilling and mining that may have verylimited access to cooking fuel or other commercially available fuelsources may be shown to benefit from the use of such a system. Equipmentthat transports materials, supplies, and workers to/from subterraneandrill sites and mines may be equipped with such a system to facilitatepreparation of food for the workers. Use of a fuel, such as hydrogenthat produces no toxic exhaust may be well suited for use in drillingand mining environments.

Agricultural production, including harvesting, planting, and the likemay also benefit from the deployment of hydrogen-based cooking and/orheating systems as described herein. Food preparation operations thatmay include heating or cooking freshly harvested foods may be shown tobenefit from an automated or semi-automated hydrogen-based cookingsystem as described herein. Such a system may be deployed on orconnected with a harvesting system, such as a produce harvester and thelike so that cooking, preserving, sterilizing, pasteurizing, drying oroptional storage operations may occur as the food is harvested. Otherdeployments, such as industrial cooking deployments, may includejob-site deployment, food truck deployment, canteen truck deployment,food production pipelines, and the like. Yet other deployments, such asindustrial cooking deployment may include residential environments, suchas nursing homes, group homes, soup kitchens, school and businesscafeterias, disaster relief food preparation stations, and the like.

The methods and systems of autonomous or semi-autonomous hydrogenproduction, storage, distribution, and use may be deployed as componentsin a smart power grid that may operate cooperatively with othercomponents of a smart grid to attempt to deliver reliable energyavailable throughout the grid. In an example, a renewable energy-basedhydrogen production system may utilize its renewable energy harvestingcomponents to deliver electricity to a smart grid based on variousfactors, such as local demand for hydrogen and the like. When arenewable energy source is available, yet hydrogen production is notcalled for (e.g., sufficient supply is stored, or an amount that isanticipated to be needed, such as based on machine learning or the likeof prior local hydrogen demand over time is expected to be produciblebefore needed), then electricity or the like produced from the renewableenergy source could be fed back into the smart grid.

Other types of industrial applications of the methods and systems ofhydrogen production, storage, distribution and use may include air andinline heaters, and the like. Exemplary environments may includedeployment for aerospace operation and testing, such as componenttemperature testing, heating, hot air curing, and the like. Productionof temperatures that emulate extremes associated with aerospace travel,such as earth atmosphere entry and the like could be replicated withsuch systems for use in component testing and the like.

Other industrial heating applications may include automotive production(e.g., heat treating components, heat shrinking and the like),automotive assembly (e.g., hot air bonding, etc.), automotive exteriorand interior customization (e.g., hot air bonding of vinyl body panelcovers, paint curing and the like), and automotive repair (e.g.,reshaping dented plastic components, such as a bumper) and the like.

Yet other industrial heating applications may include packaging,sterilization, and the like. Particular packaging uses may includehigh-speed poly-coated paperboard sealing, high-speed heat shrinkinstallations, material heat forming, curing adhesives, sterilizingbottles and cartons (e.g., through heating water and/or steamtherefore), production and packaging of pharmaceuticals, sterilizationand packaging of surgical tools and hardware, replacement dentalfeatures (e.g., crowns and the like), production and sealing ofpackaging material, and the like.

Paper and printing heating-related applications of the methods andsystems described herein may include the production of coated paper,including speed drawing the coating, adhesive activation, ink drying,paper aging, pulp drying, and the like.

Plastics and rubber production heating applications that may be shown tobenefit from the methods and systems described herein may include rubberextrusion salt removal, curing plastics, bending and forming plasticcomponents, de-flashing of molded parts and the like.

The methods and systems described herein may be used to produce heatneeded for some semiconductor and electronics production and assemblyoperations including soldering operations, such as air knife for wavesoldering, heating of printed circuit boards, lead frames, components(e.g., capacitors) for soldering/desoldering, centralized source of heatfor a multi-station desoldering system, wafer and PC board drying, heatshrink wire insulation, preheating process gases and the like. By way ofthese examples, soldering and/or brazing may require heating that may beprovided by the hydrogen-based heating systems described herein. Heatfor soldering and brazing may be generated locally at each brazingstation or may be provided from a centralized source for multiplesoldering operations, including manual and semi-manual operations.

Other heated air applications that may be suitable for application of ahydrogen-based system as described herein may include textilesindustrial uses, such as welding plastic or vinyl fabrics, heat-treatingspecialty fabrics, heat sealing fabric shipping sleeves, bondingmulti-ply fabrics and the like. Industrial hot air applications mayinclude the exemplary embodiments described herein, but may also includeother comparable applications, such as home fabric bonding, plasticsheet dispensing and the like in which heat is used to increase thetemperature of air or devices to perform various functions.

In embodiments, the methods and systems described herein that relate tohydrogen production, storage, distribution, use, regulation, monitoring,control, energy conversion, and the like may also be used for heatingoperations including immersion, circulation and customer heating.Example applications include energy production environments where fuelsources for cooking and heating may be used, such as alternative fuelsprocessing, chemical processing, mining and metals, oil, and gas,petrochemical, power generation, fuel storage, fuel distribution, heatexchangers, waste disposal, heated storage, and the like. Industrialapplications may include biopharmaceutical processing, industrialequipment (such as temperature test chambers), engine block heaters,preheating industrial burners, furnaces, kilns and the like, medicalequipment laboratory and analytic equipment, military and defenseincluding weapons, personnel management, and other military uses,production of rubber and plastics through controlled heating ofpetrochemicals and the like, transportation (such as passengercompartment temperature regulation, preheat or temperature regulation ofvehicle systems in extremely low temperature environments) and the like,water processing, waste water processing and the like. Commercialapplications of the methods and systems described herein for use asheating for immersion, circulation and the like may include integration,connection or use with commercial food equipment, building andconstruction systems, commercial marine and shipping systems andenvironments, heat-powered cooling, refrigeration, air conditioning, andother cooling applications and the like.

In addition to cooking and air heating applications, the methods andsystems of autonomous hydrolyzer operation, generated fuel storage,distribution and use described herein may also be applied to processesthat use heat from a heating element that may be powered from the fuel(e.g., hydrogen and the like) produced from the hydrolyzer.Manufacturing operations may include pharmaceutical manufacturing,industrial food manufacturing, semiconductor manufacturing, and thelike. Other heating element-like applications may include coating suchas vinyl automotive panel wrapping, molding such as injection molding,heat staking, and the like, hard tooling, heating material for extrusionoperations, combustion systems (such as flame-based combustion devices,e.g., burners that would improve on existing combustion methodsincluding improving efficiency, cost, reduce or eliminate emissions),enhance heat transfer from combustion products to the material processedfor a variety of applications, such as by applying a clean-burning fuelin proximity to the material being processed, other types of combustionsystems (e.g., non-burner types) such as catalytic combustion,combustion systems that include heat recovery devices such asself-recuperative burners, and the like.

Other applications for heat-dependent operations that may be powered bythe fuel produced from a hydrolyzer may include heat and power uses suchas integrated heating systems such as super boilers and otherapplications that deliver both heat and power to an operation (e.g.,super pressurized steam systems, and the like). Other heat utilizationapplications may include heat production include use for testingmaterials such as products for mining (e.g., heat treating drillingmachine elements), drying and moisture removal (such as clothes dryers,dehumidifiers, and the like). Other applications in which ahydrolyzer-based energy producing system may be used include heat as acatalyst for chemical reactions and processing including, withoutlimitation chemical scrubbing of exhaust from industrial systemsincluding petrochemical-based combustion systems, on-site production ofchemicals, such as high-value petroleum products from lower grade, lowercost petroleum supplies, and the like.

Other applications that may benefit from the use of an autonomoushydrogen generation system as described herein may include desalination,such as local desalination systems for pleasure boats, ferries, and thelike. Because of the high efficiency and potential for only usingrenewable energy sources, hydrogen generation-based desalination systemsmay be fully self-operative, producing hydrogen directly from a sourceof water being desalinated.

Yet other applications include using heat to power carbon capture,purification of material and systems such as a palladium electrolyzer,and the like. Industrial washing systems, such as laundry, preheatingboiler water feeds, sterilizing, sanitation, and cleaning processes forclothing, uniforms, safety gear, hospital and medical care facilities(e.g., floors and the like) may also be target applications for systemsthat include, connect to, or integrate hydrogen production, storage, anddistribution, including systems that are powered by renewable energysources and the like.

Filtering and purifying materials and equipment used in variousprocesses, such as food service, food manufacturing, pharmaceuticalproduction and handling, livestock handling and processing and the likeare also candidate application environments for the methods and systemsdescribed herein. In production environments that may rely on highlypurified materials, such a system may be applied to provide thenecessary heating or energy required. In embodiments, the methods andsystems described herein may be applied to corrosion and hydrogenembrittlement activities.

Referring to FIG. 285 environments and manufacturing uses of hydrogenproduction, storage, distribution, and use systems are depicted. Asdescribed above herein, A hydrogen system 5701 may be deployed inenvironments including industrial cooking 6006, industrial air heatersand inline heaters 6009, and industrial environments 6011. The hydrogensystem 5701 may also be used in manufacturing use cases 6005, such asheat used in manufacturing processes 6013. Deployment in environments6003 and manufacturing uses 6005 may overlap, resulting in the hydrogensystem 5701 operating in combinations of environment and use that aredepicted in FIG. 285 and described herein.

The methods and systems described herein may be used to provide hydrogendirectly from a hydrolyzer for certain uses including uses that do notrequire the introduction of oxygen. In such embodiments that may onlyrequire a hydrogen gas, the hydrogen may be produced and sent directlyfor real-time uses such as a burner for heating, industrial heatingprocesses like welding and brazing, and all other use cases that requiredirect-use hydrogen. Some other cases may include coating, tooling,extrusion, drying and the like. The methods and systems described hereinmay produce high-quality hydrogen gas for applications that require it,such as laser cutting. Other uses may include the production of hydrogengas that may then be combined with other combustible gases foroperations such as to generate a flame suitable for welding, forsupplying an oxyhydrogen torch, and the like.

In applications where both the separated hydrogen and separated oxygenmay be required for different purposes, the generation, storage,distribution and/or heating (e.g., cooking) system may directindependently both gases to their appropriate process uses. An examplecould be an electrolyzer on a submarine where the hydrogen may be usedfor a burner, and the oxygen used in the submarines air circulationsystem, and the like. In yet other embodiments the oxygen and hydrogenthat have been separated during the hydrolysis process may need to berecombined under a protocol that produces a desired combination and rateof the combination of oxygen and hydrogen. One such example isOxy-Hydrogen welding.

In embodiments, other examples of time-shifted uses of electrolyzerproducts that may benefit from and/or include hydrogen storage mayinclude storing hydrogen in its non-compressed state, in its gaseousstate, in its compressed liquid state or combinations thereof in a smalltank that is part of a cooking or other industrial system, in a largertank on or near the cooking system, or transported to very large holdingtanks at a facility that is not nearby. Further examples of hydrogenstorage technology may include absorbing the hydrogen by a substrate.The substrate may then be stored in a small tank or other substratestorage facility that may be part of the cooking system, in a largertank on or near the cooking system, transported to very large holdingtanks at a facility that is not nearby, or distributed across aplurality of small, medium, and large storage facilities that mayfacilitate local access to the stored energy. At the appropriate time,the substrate may be heated and the hydrogen may return to its originalgaseous state.

Cooking and other heating systems that may use hydrogen as one of aplurality of sources of fuel may participate in automatically selectingamong the sources of fuel. These systems may include processingcapabilities that are connected to various information sources that mayprovide data regarding factors that may be beneficial to consider whendetermining which energy source to select. Determining which energysource to select may be based, for example on a single factor, such as acurrent price for one or more of the sources of energy. An energy sourcethat provides sufficient energy at a lowest current price may beselected. In embodiments, a cooking or other heating system mayautomatically, under computer control, be configured for the selectedsource of energy. In an example, if hydrogen is selected, connections toa source of hydrogen may be activated, while connections to othersources may be deactivated. Likewise, burners, heater controls, heat andsafety profiles, cooking times, and a range of other factors may beautomatically adjusted based on the selected energy source. If during acooking or heating operation, another source of energy is found to beless costly (such as electricity), systems may automatically bereconfigured for use of the other source of energy. Gas-fired heatersmay be disabled and electric heating elements may be energized tocontinue the cooking and/or heating operation with minimal interruption.Such hybrid energy source cooking and/or heating processes may require adistinct protocol for completing a cooking or heating process based onthe new source of energy.

Alternatively, automatic selection of a fuel source may be based on amultitude of factors. These factors may be applied to a fuel sourceselection algorithm that may process individually, in groups, or incombination a portion of the factors. Example factors may include theprice of other energy sources, including energy sources that areavailable to the cooking and heating system as well as those that arenot directly available. In this way, selecting an energy source may bedriven by other considerations, such as which energy source is betterfor the environment, and the like. In embodiments, an automatic energysource selection may be based, at least in part on the anticipatedavailability of an energy source. In embodiments, predictions of energyoutage, such as brownouts, may be based on a range of factors, includingdirect knowledge of scheduled brownouts and the like. Such predictionsmay also be based on prior experience regarding the availability of thesource(s) of energy, which may be applied to machine learning algorithmsthat may provide predictions of future energy availability. Yet otherfactors that may be applied to an algorithm for automaticallydetermining a source of energy may include availability of a source ofwater for producing hydrogen, availability of renewable energy (e.g.,based on a forecast for sunlight, winds, and the like), level and/orintensity of need of the energy, anticipate level of need over a futureperiod of time, such as the next 24 hours and the like. If an anticipateneed over a future period of time includes large swings in demand overthat timeframe, each peak in demand may be individually analyzed.Alternatively, an average or other derivatives of the demand over timemay be used to determine a weighting for the various sources of energy.

In addition to energy selection for direct application to cooking andheating, energy selection for operating a hydrolyzer to produce hydrogenmay be automated. Energy sources that may be included in such anautomated selection process may include solar energy, wind energy,hydrogen energy, sulfur dioxide, electricity (such as from anelectricity grid), natural gas, and the like. In embodiments, analgorithm that may facilitate automatic energy selection may receiveinformation about each energy source, such as availability, costs,efficiency, and the like that may be processed by, for example comparingthe information to determine which energy source provides the best fitfor operating the hydrolyzer in a given time period. By way of thisexample, the algorithm may favor energy sources that are more reliable,more available, and lower costs than those that are less reliable, lessavailable, and costlier. In embodiments, combinations of these threefactors may result in certain sources being selected. If a demand forreliable energy at a particular time is weighted more highly than price,for example, a costlier energy source may be automatically selected dueto it being more reliably available. An automatic fuel selectionalgorithm may also produce recommendations for fuel selection and ahuman or other automated process may make a selection. In an example, anautomated fuel selection algorithm may recommend a fuel that is lesscostly, but may be somewhat less reliable than another source; however,given the weighting or other aspects of the available information aboutthe sources, such a recommendation may meet acceptance criteria of thealgorithm.

Methods and systems described herein may be associated with methods andsystems for automatic selection of an energy source, such as a methodfor determining an optimal use of renewable energy (such as solar, wind,geothermal, hydro and the like) or non-renewable fuel. In embodiments, aselection of energy source to power an onsite, standalone cooking orheating system may be based on a variety of factors including access anddistance to a source of renewable energy source as a primary source,directly to the cooking system. As an example, while production costdata available regarding hydro-based renewable energy may support itsselection, a delivery network may not be in place or may charge asubstantive premium for access to that particular renewable source;therefore hydro-based renewable energy may not be an optimal use.

In embodiments, other factors include pricing and amount of electricityrequired to use the cooking system and electrolyzer and the; ability ofthe source to match up availability with demand for generated power isrequired for both sustained periods of usage as well as short-termrequirements. In embodiments, other factors that may impact an automatedenergy source selection process may include availability and ability toreuse excess heat from the cooking system and/or other nearby industrialfacilities. In embodiments, excess heat may include exhaust heat, sulfurdioxide byproduct and the like that may be used to generate heat througha heat exchange process. In embodiments, another set of criteria fordetermining which energy source may be optimal for use by a cookingsystem as described herein may include comparing the need for short-termaccessibility to power at arbitrary times throughout the day, comparedto limiting timing of demand to power given timing and availability ofpower sources, such as nearby power sources. Sulfur dioxide as a wasteheat byproduct may be used in a heat transfer process to recapture heatfrom the sulfur dioxide gas; however, it may also be applied directly tothe hydrolyzer system to produce hydrogen. In embodiments, the sulfurdioxide gas may be applied directly to the hydrolyzer system to producehydrogen and reduce the sulfur dioxide gas as a tool for environmentalabatement by reducing the amount of the sulfur dioxide gas and use thegenerated hydrogen to burn trash and other items for its removal, forelectricity generation, and the like.

In embodiments, external systems, such as information systems may beassociated with or connected to hydrogen production, storage,distribution, and use systems as described herein. Information systemsmay receive information from all aspects and system processes including,energy selection (such as automated energy selection) including actualresults as compared to predicted results, energy consumption, hydrogengeneration for each type of energy source (solar, hydro-based, wind,exhaust gas, including sulfur dioxide use, and the like), hydrogenrefinement processes, hydrogen storage (including compressed, naturalstate storage, substrate infusion-based, and the like), hydrogendistribution, uses, combinations with other fuel sources (such ashydrogen with another flammable energy medium) and the like, uses of thehydrogen including timing, costs, application environment, and the like.

In embodiments, communication to and from external systems may bethrough exchange of messages that may facilitate remote monitoring,remote control and the like. By way of this example, messages mayinclude information about a source of the message, a destination, anobjective (e.g., control, monitoring, and the like), recommended actionsto take, alternate actions to take, actions to avoid, and the like.

In embodiments, methods and systems related to hydrogen production,storage, distribution and use may include, be associated with, orintegrate improvement features that may provide ongoing improvements insystem performance, quality and the like. In embodiments, improvementfeatures may include process control and heat recovery, flow control andprecision control, safety, reliability and greater service availability,process and output quality including output consistency. Other featuresthat may be provided and/or be integrated with the hydrogen-basedsystems described herein may include data collection, analysis, andmodeling for improvement, data security, cyber security, networksecurity to avoid external attacks on control systems and the like,monitoring and analysis to facilitate preventive maintenance and repair.

In embodiments, integration and/or access to data processing systemsthat also have access to third-party data may be included in the methodsand systems described herein. By monitoring data collected from sensors,time of day, weather conditions, and other data sources may be used withspecific rule sets to trigger activation and/or stoppage of hydrogen use(e.g., cooking) operations. In embodiments, data may be accumulated in acontinuous feedback loop that may capture data for a range of metricsassociated with operations, such as cooking operations and the like. Inembodiments, analysis and control of activation of such a system mayfactor in the actual requirements and timing when a cooking system needsto be used (such as when a meal is being prepared, such as breakfast, orwhen heating is required for an industrial operation, such as at thestart of a new work shift and the like.

In embodiments, data collection, monitoring, process improvement,quality improvement, and the like may also be performed during operationof such a system. In an example, once a cooking system is activated, thesystem may be able to determine the best way to receive the heatrequired to perform the process at hand at that particular moment intime. Receiving the heat required to perform the process may be selectedfrom a variety of heat sources including in-line hydrogen production,stored hydrogen consumption, combined energy utilization and the like.In embodiments, cooking elements with a mix of hydrogen and non-hydrogenheat burners may be automatically controllable so that the system shouldbe able to automatically, using machine learning for example andcontinuous monitoring, decide to use one or the other source or acombination thereof.

Further in this example, a smart cooktop may include burners forhydrogen and for liquid propane. In embodiments, methods and systems forcooking operation may automatically activate the appropriate burnerbased on fuel selection (e.g., hydrogen burner or the liquid propaneburner.). Operating such a cooking or heating system may be done by acomputer enabled controller that may process factors including time ofday, spot-pricing energy costs for each alternative, length of processinvolved, meeting 100% green requirements, potential hazardous use offlame depending on location of cooking system, other security features,and the like. To facilitate continuous improvement during operationalcontrol, data analysis may be performed on any or all aspects of thesystem. In an example, if the electrolyzer is not activated, sensors maycapture information about the liquid propane burner that is being used.In embodiments, this single data capture example indicates that while itis desirable to collect information about all operational aspects toavoid missing information, practical considerations enable more focuseddata collection and analysis. In embodiments, every activity and actionby the cooking system and heating element may be captured, recorded,measured, and used to inform actions such as quality improvement and thelike.

In embodiments, information may be provided for one or more deploymentsof this cooking system to facilitate self-improvement and real-timedecision making. In embodiments, information captured may also be storedand used in time-series analysis and the like to determine patterns thatmay indicate opportunities for improvement. In embodiments, datacaptured for a plurality of deployments may be used for creating andupdating models that may be used for computer-generated simulations andthe like. These models may be applied to design processes and the like.In embodiments, continuous improvement modifications may be activated bymachine-to-machine learning programs, human improvement efforts,instructional improvement and/or modifications, and the like.

Systems and methods for using wearable devices for mobile datacollection within an environment for industrial IoT data collection arenext described with respect to FIGS. 286 to 289. Referring first to FIG.286, a data collection system may include one or more wearable devicesconfigured to act as mobile data collectors within an environment forindustrial IoT data collection. For example, the one or more wearabledevices may transmit data to, receive data from, transmit commands to,receive commands from, be under the control of, communicate controlsfor, or otherwise communicate with the industrial IoT data collection,monitoring and control system 10. Methods and systems are disclosedherein for data collection using wearable devices, including a singlewearable device having a single sensor for recording state-relatedmeasurements (otherwise “measurements of states” or “statemeasurements,” as noted below) within the environment for industrial IoTdata collection, a single wearable device having multiple sensors forrecording state-related measurements within the environment forindustrial IoT data collection, multiple wearable devices each having asingle sensor for recording state-related measurements within theenvironment for industrial IoT data collection, and multiple wearabledevices each having one or more sensors for recording state-relatedmeasurements within the environment for industrial IoT data collection.For example, a wearable device may be a wearable haptic or multi-sensoruser interface for an industrial sensor data collector, with vibration,heat, electrical, and/or sound outputs, and any other suitable outputs.In another example, a wearable device may be any other suitable device,component, unit, or other computational aspect having a tangible formand which is configured or otherwise able to be used by disposing on aperson within an industrial environment, regardless of the period oftime of such use. For example, a wearable device may be an article ofclothing or a device included within an article of clothing. In anotherexample, a wearable device may be an accessory article or a deviceincluded within an accessory article. Examples of articles of clothingthat the wearable device can be or be included within include, withoutlimitation, shirts, vests, jackets, pants, shorts, gloves, socks, shoes,protective outerwear, undergarments, undershirts, tank tops, and thelike. Examples of accessory articles that the wearable device can be orbe included within include, without limitation, hats, helmets, glasses,goggles, vision safety accessories, masks, chest bands, belts, liftsupport garments, antennae, wrist bands, rings, necklaces, bracelets,watches, brooches, neck straps, backpacks, front packs, arm packs, legpacks, lanyards, key rings, headphones, hearing safety accessories,earbuds, earpieces, and the like. Regardless of the particular form, awearable device according to this disclosure includes one or moresensors for recording state-related measurements of an environment forindustrial IoT data collection. For example, the one or more sensors ofa wearable device described in this disclosure can measure states withrespect to equipment within an industrial IoT environment or withrespect to the industrial IoT environment itself. As used herein, ameasurement of a state recorded using a sensor (e.g., of a wearabledevice or of any other suitable data collector) refers to informationrelating to a target of the environment for industrial IoT datacollection. That is, the information directly or indirectly indicates astate of a target, or may otherwise be used to indicate a state of atarget. For example, the information may indirectly indicate a state ofa target where it is processed or otherwise used to identify ordetermine the state of the target. As used herein, the recording of ameasurement using a sensor (e.g., of a wearable device or of any othersuitable data collector) refers to the use of the sensor in making themeasurement available for further processing. For example, recording ameasurement using a sensor may refer to one or more of generating dataindicative of the measurement, transmitting a signal indicative of themeasurement, or otherwise obtaining values for the measurement.

A number of wearable devices 14000 are located within the environmentfor industrial IoT data collection. In some scenarios, the wearabledevices 14000 may be wearable devices issued by an operator of theenvironment for industrial IoT data collection. Alternatively, thewearable devices 14000 may be wearable devices owned by workers selectedto perform tasks within the environment for industrial IoT datacollection. As shown in FIG. 286, the wearable devices 14000 may includeany combination of a single wearable device with a single sensor 14002,a single wearable device with multiple sensors 14004, a combination ofwearable devices each with a single sensor 14006, and a combination ofwearable devices each with one or more sensors 14008. However, inembodiments, the wearable devices 14000 may include different wearabledevices. For example, in embodiments, the wearable devices 14000 mayomit the combination of wearable devices each with a single sensor 14006and/or the combination of wearable devices each with one or more sensors14008. For example, the wearable devices 14000 may be limited toindividual wearable devices rather than combinations of wearable devicesthat offer combined, improved or otherwise different functionality whencompared to each of the constituent wearable devices taken individually.In another example, in embodiments, the wearable devices 14000 may omitthe single wearable device with the single sensor 14002 and/or thesingle wearable device with multiple sensors 14004. For example, thewearable devices 14000 may be limited to combinations of wearabledevices rather than individual devices (e.g., where specificcombinations of the wearable devices are identified as being valuable inparticular contexts or otherwise for recording particular state-relatedmeasurements within the environment for industrial IoT data collection).Communications and other transfers of data between the wearable devices14000 and the devices that receive the output from the wearable devices,or otherwise between the sensors within the wearable devices 14000 and adevice that receives the output of those sensors, may be wireless orwired and may include such standard communication technologies as 802.11and 900 MHz wireless systems, Ethernet, USB, firewire, and so on.

In embodiments, different wearable devices 14000 may be configured torecord certain types of state-related measurements of some or all of thetargets (e.g., devices or equipment) within the environment forindustrial IoT data collection. For example, some of the wearabledevices 14000 may be configured to record state-related measurements oftargets based on vibrations measured with respect to some or all of thetargets. A vibration measured with respect to a target may refer to,without limitation, a frequency at which all or a portion of the targetvibrates, a waveform derived from a vibration envelope associated withthe target, vibration level changes, and the like. In another example,some of the wearable devices 14000 may be configured recordstate-related measurements of targets based on temperatures measuredwith respect to some or all of the targets. A temperature measured withrespect to a target may refer to, without limitation, an internal orexternal temperature of all or a portion of the target, an operatingtemperature of the target, a temperature measured within an area aroundthe target, and the like. In another example, some of the wearabledevices 14000 may be configured to record state-related measurements oftargets based on electrical or magnetic outputs measured with respect tosome or all of the targets. An electrical or magnetic output measuredwith respect to a target may refer to, without limitation, a level orchange in an electromagnetic field associated with the target, an amountof electricity or magnetic quality output from the target or otherwiseemitted by the target, and the like. In another example, some of thewearable devices 14000 may be configured to record state-relatedmeasurements of targets based on sound outputs measured with respect tosome or all of the targets. A sound output measured with respect to atarget may refer to, without limitation, an audible or inaudiblefrequency corresponding to a sound wave generated by or in connectionwith the target, a sound wave emitted by a change in operation of thetarget, and the like. In another example, some of the wearable devices14000 may be configured to record state-related measurements of targetsbased on outputs other than vibrations, temperatures, electrical ormagnetic, or sound, as measured with respect to some or all of thetargets.

Alternatively, or additionally, different wearable devices 14000 may beconfigured to record some or all state-related measurements of certaintypes of the targets within the environment for industrial IoT datacollection. For example, some of the wearable devices 14000 may beconfigured to record some or all state-related measurements fromagitators (e.g., turbine agitators), airframe control surface vibrationdevices, catalytic reactors, compressors and the like. In anotherexample, some of the wearable devices 14000 may be configured to recordsome or all state-related measurements from conveyors and lifters,disposal systems, drive trains, fans, irrigation systems, motors, andthe like. In another example, some of the wearable devices 14000 may beconfigured to record some or all state-related measurements frompipelines, electric powertrains, production platforms, pumps (e.g.,water pumps), robotic assembly systems, thermic heating systems, tracks,transmission systems, turbines, and the like. In embodiments, thewearable devices 14000 may be configured to record some or allstate-related measurements of certain types of industrial environments.For example, an industrial environment having targets with statesmeasured using the wearable devices 14000 may include, but is notlimited to, a manufacturing environment, a fossil fuel energy productionenvironment, an aerospace environment, a mining environment, aconstruction environment, a ship environment, a shipping environment, asubmarine environment, a wind energy production environment, ahydroelectric energy production environment, a nuclear energy productionenvironment, an oil drilling environment, an oil pipeline environment,any other suitable energy product environment, any other suitable energyrouting or transmission environment, any other suitable industrialenvironment, a factory, an airplane or other aircraft, a distributionenvironment, an energy source extraction environment, an offshoreexploration site, an underwater exploration site, an assembly line, awarehouse, a power generation environment, a hazardous wasteenvironment, and the like.

The combination of wearable devices each with a single sensor 14006and/or the combination of wearable devices each with one or more sensors14008 may represent a combination of wearable devices selected for usetogether within the environment for industrial IoT data collection. Forexample, the combination of wearable devices each with a single sensor14006 and/or the combination of wearable devices each with one or moreof the sensors 14008 may represent all or a portion of an industrialuniform to be worn by a worker performing one or more tasks within theenvironment for industrial IoT data collection. For example, thecombination of wearable devices each with the single sensor 14006 and/orthe combination of wearable devices each with one or more of the sensors14008 may include one of each of a number of wearable devices to be wornby the user (e.g., one hat, one shirt, one pair of pants, one pair ofshoes, one vest, one necklace, one bracelet, one backpack, or more orfewer wearable devices). Embodiments of this disclosure may contemplateindustrial uniforms as including other possible combinations of thewearable devices as the combination of wearable devices each with thesingle sensor 14006 and/or the combination of wearable devices each withone or more of the sensors 14008.

In embodiments, the combined use of multiple sensors, either as thecombination of wearable devices each with the single sensor 14006 and/oras the combination of wearable devices each with one or more of thesensors 14008, may introduce extended or additional functionality forindustrial IoT data collection. Thus, in some of those embodiments, anindustrial uniform may include functionality beyond what is provided bythe individual sensors that are integrated within the industrialuniform. For example, the output of wearable devices with sensors forrecording state-related measurements of the same target may bepre-processed by a central processing software or hardware aspectintegrated within or otherwise corresponding to the industrial uniform(e.g., a collective processing mind, as described below). For example,the central processing software or hardware aspect integrated within orotherwise corresponding to the industrial uniform may process the outputof multiple wearable devices to determine whether the output is the samefor a particular observed measurement of a target. Where one of thoseoutputs is more than a threshold deviation from the other outputs, thatdeviated output may be discarded. For example, the discarded output mayrepresent output produced using a sensor that suffered from interferenceor other issues while recording the state-related measurement of thetarget. In another example, the central processing software or hardwareaspect integrated within or otherwise corresponding to the industrialuniform may process different types of output (e.g., recorded based ondifferent targets or different state-related measurement types, forexample, vibrational versus temperature) of multiple wearable devices todetermine or identify a state of the target. For example, it may be thecase that a state is indicated by a combination of outputs. In such ascenario, a first output from a first wearable device can be combined orotherwise processed along with a second output from a second wearabledevice to determine or identify the state of the target. Differentcombinations of wearable devices may be identified as differentindustrial uniforms, in which each of the industrial uniforms may havethe same or different capabilities with respect to recording types ofstate-related measurements of targets. In yet another example, theintegration of multiple wearable devices within an industrial uniformallows for the concurrent or substantially concurrent processing ofstate-related measurements recorded using those wearable devices.

The state-related measurements using the wearable devices 14000 may bemade available over a network 14010 (e.g., without the need for externalnetworks). The network 14010 may be a MANET (e.g., the MANET 20 shown inFIG. 2 or any other suitable MANET), the Internet (e.g., the Internet110 shown in FIG. 3 or any other suitable Internet), or any othersuitable type of network, or any combination thereof. For example, thenetwork 14010 may be used to receive state-related measurements recordedusing the wearable devices 14000. The network 14010 may then be used totransmit some or all of those received state-related measurements toother components of the data collection system 102. For example, thenetwork 14010 may be used to transmit some or all of the receivedstate-related measurements to a data pool 14012 (e.g., the data pool 60shown in FIG. 2 or any other suitable data pool) for storage of thosereceived state-related measurements. In another example, the network14010 may be used to transmit some or all of the received state-relatedmeasurements to one or more servers 14014 corresponding to theenvironment for industrial IoT data collection. The servers 14014 mayinclude one or more hardware or software server aspects. For example,the servers 14014 to which the received state-related measurements aretransmitted may include intelligent systems 14016 that process thereceived state-related measurements. The intelligent systems 14016 mayprocess the received state-related measurements in any suitable manner,including using artificial intelligence processes, machine learningprocesses, and/or other cognitive processes to identify informationwithin or otherwise associated with the received state-relatedmeasurements. In embodiments, after processing the receivedstate-related measurements, the servers 14014 to which the receivedstate-related measurements are transmitted may transmit the processedinformation or data indicative of the processed information to othersystems (e.g., for storage or analysis). The data indicative of theprocessed information from the servers 14014 may include, for example,output or other results of the artificial intelligence processes,machine learning processes, and/or other cognitive processes.

In embodiments, some or all of the wearable devices 14000 may includeintelligent systems 14018 for processing the state-related measurementsrecorded using those wearable devices 14000 before transmitting thoserecorded state-related measurements (e.g., over the network 14010) orany other suitable communication mechanism. For example, some or all ofthe wearable devices 14000 may integrate artificial intelligenceprocesses, machine learning processes, and/or other cognitive processesfor analyzing the state-related measurements recorded thereby. Theprocessing by the intelligent systems 14018 of the wearable devices14000 may be or be represented within a pre-processing step of theindustrial IoT data collection, monitoring and control system 10. Forexample, the pre-processing may be selectively performed by certaintypes of the wearable devices 14000 to pre-process the recordedstate-related measurements, for example, to identify redundantinformation, irrelevant information, or insignificant information. Inanother example, the pre-processing may be automated for certain typesof the wearable devices 14000 to pre-process the recorded state-relatedmeasurements, for example, to identify redundant information, irrelevantinformation, or insignificant information. In another example, thepre-processing may be selectively performed for certain types ofstate-related measurements recorded by any of the wearable devices 14000to pre-process the recorded state-related measurements, for example, toidentify redundant information, irrelevant information, or insignificantinformation. In another example, the pre-processing may be automated forcertain types of state-related measurements recorded by any of thewearable devices 14000 to pre-process the recorded state-relatedmeasurements, for example, to identify redundant information, irrelevantinformation, or insignificant information.

In embodiments, some or all of the wearable devices 14000 may includesensor fusion functionality. For example, the sensor fusionfunctionality may be embodied as the on-device sensor fusion 80. Forexample, state-related measurements recorded using multiple analogsensors of one or more of the wearable devices 14000 (e.g., the multipleanalog sensors 82 shown in FIG. 4 or any other suitable sensor) may belocally or remotely processed (e.g., using artificial intelligenceprocesses, machine learning processes, and/or other cognitiveprocesses), which may be embodied within the wearable devices 14000themselves, within the servers 14014, within both, or within any othersuitable hardware or software. For example, the output of the sensorsintegrated within the wearable devices 14000 may be provided directly tothe on-device sensor fusion aspect 80. The sensor fusion functionalitymay be embodied by a pre-processing step that is performed prior to theartificial intelligence processes, machine learning processes, and/orother cognitive processes. In embodiments, the sensor fusionfunctionality may be performed using a MUX. For example, each of thesingle wearable devices with multiple sensors 14004 may include its ownMUX for combining state-related measurements recorded using differentindividual sensors of those multiple sensors. In another example, someor all of the individual wearable devices within the combination ofwearable devices each with one or more sensors 14008 may include its ownMUX for combining state-related measurements recorded using differentindividual sensors of those multiple sensors. In some such embodiments,the MUX may be internal to those wearable devices. In some suchembodiments, the MUX may be external to those wearable devices.

In embodiments, the wearable devices 14000 may be controlled by orotherwise used in connection within a host processing system 112 shownin FIG. 6 (or any other suitable host system). The host processingsystem 112 may be locally accessible over the network 14010.Alternatively, the host processing system 112 may be remote (e.g.,embodied in a cloud computing system), may be accessible using one ormore network infrastructure elements (e.g., access points, switches,routers, servers, gateways, bridges, connectors, physical interfaces andthe like), and/or may use one or more network protocols (e.g., IP-basedprotocols, TCP/IP, UDP, HTTP, Bluetooth, Bluetooth Low Energy, cellularprotocols, LTE, 2G, 3G, 4G, 5G, CDMA, TDSM, packet-based protocols,streaming protocols, file transfer protocols, broadcast protocols,multi-cast protocols, unicast protocols, and the like). In embodiments,the state-related measurements recorded using the wearable devices 14000may be processed using a network coding system or method, which may beembodied internally or externally with respect to the host processingsystem 112. For example, the network coding system can process themeasurements recorded using the wearable devices 14000 based on theavailability of networks for communicating those recorded state-relatedmeasurements, based on the availability of bandwidth and spectrum forcommunicating those recorded state-related measurements, based on othernetwork characteristics, or based on some combination thereof.

In embodiments, the state-related measurements recorded using thewearable devices 14000 may be pulled from the wearable devices 14000 byan upstream device (e.g., a client device or other software or hardwareaspect used to review, analyze, or otherwise view the state-relatedmeasurements). For example, the wearable devices 14000 may not activelytransmit the state-related measurements that are received (e.g., at theservers 14014, the data pool 14012, or any other suitable hardware orsoftware component that receives the state-related measurements recordedusing the wearable devices 14000). Rather, the transmission of thestate-related measurements from the wearable devices 14000 may be causedby commands received at the wearable devices 14000 (e.g., from servers14014 or from other hardware or software of the data collection system102). For example, a data collector, which may be fixed within aparticular location of the environment or which may be mobile withrespect to the environment, may be configured to pull state-relatedmeasurements recorded by various wearable devices 14000. For example,the wearable devices 14000 may continuously, periodically, or otherwiseat multiple times record state-related measurements within theenvironment for industrial IoT data collection. The data collector may,at fixed intervals, at random times, or otherwise, transmit one or morecommands to some or all of the wearable devices 14000 (e.g., to pullsome or all of the state-related measurements recorded by those wearabledevices 14000 since the last time state-related measurements were pulledtherefrom). Alternatively, the data collector may, at those fixedintervals, at those random times, or otherwise, transmit the one or morecommands to a collective processing mind 14020 associated with thewearable devices 14000. For example, the collective processing mind14020 may be or include a hub for receiving the state-relatedmeasurements recorded using some or all of the wearable devices 14000.In another example, the commands, when processed using individualwearable devices 14000 or by the collective processing mind 14020 of thewearable devices 14000, cause the recorded state-related measurements ordata representative thereof to be transmitted from the wearable devices14000. For example, the collective processing mind 14020 may beconfigured to pull the state-related measurements from some or all ofthe wearable devices 14000 (e.g., at fixed intervals, at random times,or otherwise). The collective processing mind 14020 may then transmitthe state-related measurements pulled from the wearable devices 14000(e.g., to the servers 14014, the data pool 14012, or the other hardwareor software component selected or otherwise configured to receive thestate-related measurements).

In embodiments, the state-related measurements recorded using thewearable devices 14000 may be transmitted from the wearable devices14000 responsive to requests for those state-related measurements. Forexample, the collective processing mind 14020 may, at fixed intervals,at random times, or otherwise, transmit a request for recordedstate-related measurements to some or all of the wearable devices 14000.The processors of some or all of the wearable devices 14000 to which therequest is sent may process the request to determine which state-relatedmeasurements to transmit. For example, data indicative of a time of amost recent request for recorded state-related measurements may beaccessed by those processors. The processors may then compare that timeto a time at which the new request is received from the collectiveprocessing mind 14020. The processors may then query a data store forstate-related measurements recorded between the two times. Theprocessors may then transmit those state-related measurements inresponse to the request. In another example, the processors may identifya most recent set of state-related measurements recorded using thecorresponding wearable devices 14000 and transmit those state-relatedmeasurements in response to the request. In another example, datacollectors within the data collection system 10 may transmit the requestdirectly to the wearable devices 14000. In yet another example, the datacollectors may transmit the request to the collective processing mind14020. The collective processing mind 14020 may process the request todetermine select individual wearable devices 14000 which were used torecord the requested state-related measurements. The collectiveprocessing mind 14020 may then transmit certain state-relatedmeasurements in response to the request by, for example, querying astorage for some or all of the state-related measurements recorded usingthose select individual wearable devices 14000. Alternatively, thecollective processing mind 14020 may process the request to determinewhich of the state-related measurements recorded by some or all of thewearable devices 14000 to transmit in response to the request (e.g.,based on a time of the request). For example, the collective processingmind 14020 can compare the time of the request to a time of a mostrecent request for recorded state-related measurements. The collectiveprocessing mind 14020 can then retrieve the state-related measurementsrecorded in between those times and transmit the retrieved state-relatedmeasurements in response to the request.

In embodiments, the state-related measurements may be pushed from thewearable devices 14000 to an upstream device (e.g., a client device orother software or hardware aspect used to review, analyze, or otherwiseview the state-related measurements). For example, the wearable devices14000 may actively transmit the state-related measurements that arereceived (e.g., to the servers 14014, the data pool 14012, or any othersuitable hardware or software component that receives the state-relatedmeasurements recorded using the wearable devices 14000) without suchreceiving hardware or software component requesting those state-relatedmeasurements or otherwise causing the wearable device to transmit thosestate-related measurements based on a command. For example, some or allof the wearable devices 14000 may transmit state-related measurements ona fixed interval, at random times, immediately upon the recording ofthose state-related measurements, some amount of time after recordingthose measurements, upon a determination that a threshold number ofstate-related measurements have been recorded, or at other suitabletimes. In some such embodiments, the wearable devices 14000, either bythemselves or using the collective processing mind 14020, may push therecorded state-related measurements in response to detecting a nearproximity of a data collection router 14014.

For example, referring next to FIG. 287, the collective processing mind14020 may include a detector 14022 configured to detect a near proximityof a target 14024 (e.g., one of the devices 13006 shown in FIG. 180 orany other suitable target) with respect to one or more of the wearabledevices 14000. For example, upon such a detection, the collectiveprocessing mind 14020 may send a signal to the one or more of thewearable devices 14000 to record and transmit state-related measurementsof receipt at a data collection router 14026. Alternatively, upon such adetection, the collective processing mind 14020 may query a data storeto retrieve state-related measurements and then transmit thosestate-related measurements of receipt at the data collection router14026. In either case, the data collection router 14026 forwards thereceived state-related measurements to the servers 14014, the data pool14012, or any other suitable hardware or software component. In anotherexample, upon such a detection, the collective processing mind 14020 maysend the signal directly to the servers 14014, the data pool 14012, orthe other hardware or software component, for example, to bypass thedata collection router 14026 or where the data collection router 14026is omitted.

Referring next to FIG. 288, in embodiments, the collective processingmind 14020 may be omitted. In some of these embodiments, the wearabledevices 14000 may detect the near proximity of the target 14024. Uponsuch detection, the wearable devices 14000 may record state-relatedmeasurements of the target 14024 (e.g., vibrations, temperature,electrical or magnetic output, sound output, or the like). The recordedstate-related measurements can be transmitted over the network 14010(e.g., to the data pool 14012, the servers 14014, or any other suitablehardware or software component). Alternatively, the recordedstate-related measurements can be transmitted to the data collectionrouter 14026, for example, where the network 14010 is unavailable orwhere the data collection router 14026 is configured to receive and/orpre-process the recorded state-related measurements from the wearabledevices 14000. The data collection router 14026 may be one of a numberof data collection routers 14026 located throughout the environment forindustrial IoT data collection. For example, the data collection router14026 may be the data collection router 14026 configured to transmitstate-related measurements specifically recorded for the target 14024.

Referring next to FIG. 289, various aspects of functionality ofintelligent systems 14028 used to process output of the wearable devices14000 are disclosed. In embodiments, the intelligent systems 14028include a cognitive learning module 14030, an artificial intelligencemodule 14032, and a machine learning module 14034. The intelligentsystems 14028 may include additional or fewer modules. The intelligentsystems 14028 may, for example, be the intelligent systems 14018 or theintelligent systems 14016 shown in FIG. 286 or other intelligentsystems. Although shown as separate modules, in embodiments, there maybe an overlap between some or all of the cognitive learning module14030, the artificial intelligence module 14032, and the machinelearning module 14034. For example, the artificial intelligence module14032 may include the machine learning module 14034. In another example,the cognitive learning module 14030 may include the artificialintelligence module 14032 (and, in embodiments, therefore, the machinelearning module 14034). The wearable devices 14000 may include anynumber of wearable devices. For example, as shown, the wearable devices14000 include a first wearable device 14000A, a second wearable device14000B, and an Nth wearable device 14000N, where N is a number greaterthan two. The intelligent systems 14028 receives the output of thewearable devices 14000A, 14000B, . . . 14000N. In particular, one ormore of the modules 14030, 14032, and 14034 of the intelligent systems14028 receives data generated by and output from one or more of thewearable devices 14000A, 14000B, . . . 14000N. The output from thewearable devices 14000A, 14000B, . . . 14000N may, for example, includestate-related measurements recorded using the wearable devices 14000A,14000B, . . . 14000N (e.g., state-related measurements of equipmentwithin an environment for industrial IoT data collection). Inembodiments, the output from the wearable devices 14000A, 14000B, . . .14000N may be processed by all three of the modules 14030, 14032, and14034 of the intelligent systems 14028. In embodiments, the output fromthe wearable devices 14000A, 14000B, . . . 14000N may be processed byonly one of the modules 14030, 14032, and 14034 of the intelligentsystems 14028. For example, the particular one of the modules 14030,14032, and 14034 of the intelligent systems 14028 to use to process theoutput from the wearable devices 14000A, 14000B, . . . 14000N may beselected based on the wearable device used to generate that output, theequipment measured in generating that output, the values of the output,other selection criteria, and the like.

A knowledge base 14036 may be updated based on output from theintelligent systems 14028. The knowledge base 14036 represents a libraryor other set or collection of knowledge related to the environment ofthe industrial IoT data collection, including equipment within thatenvironment, tasks performed within that environment, personnel havingthe skill to perform tasks within that environment, and the like. Theintelligent systems 14028 can process the state-related measurementsrecorded using the wearable devices 14000A, 14000B, . . . 14000N tofacilitate knowledge gathering for expanding the knowledge base 14036.For example, the modules 14030, 14032, and 14034 of the intelligentsystems 14028 can process those state-related measurements againstexisting knowledge within the knowledge base 14036 to update orotherwise modify information within the knowledge base 14036. Theintelligent systems 14028 may use intelligence and machine learningcapabilities (e.g., of the machine learning module 14034 or as describedelsewhere in this disclosure) to process state-related measurements andrelated information based on detected conditions (e.g., conditionsinformed by the wearable devices 14000 and/or provided as training data)and/or state information (e.g., state information determined by amachine state recognition system that may determine a state, forexample, information relating to an operational state, an environmentalstate, a state within a known process or workflow, a state involving afault or diagnostic condition, and the like). This may includeoptimization of input selection and configuration based on learningfeedback from the learning feedback system, which may include providingtraining data (e.g., from a host processing system or from other datacollection systems either directly or from the host processing system)and may include providing feedback metrics (e.g., success metricscalculated within an analytic system of the host processing system).Examples of host processing systems, learning feedback systems, datacollection systems, and analytic systems are described elsewhere in thisdisclosure. Thus, the intelligent systems 14028 can be used to updateworkflows of tasks assigned and performed within the industrial IoTenvironment based on output from the wearable devices 14000A, 14000B, .. . 14000N.

In embodiments, the intelligent systems 14028, either within one of themodules 14030, 14032, and 14034 or otherwise, may include otherintelligence or machine learning aspects. For example, the intelligentsystems 14028 may include one or more of a you only look once (YOLO)neural network, a YOLO convolutional neural network (CNN), a set ofneural networks configured to operate on or from a FPGA, a set of neuralnetworks configured to operate on or from a FPGA and graphics processingunit (GPU) hybrid component, a user configurable series and parallelflow for a hybrid neural network (e.g., configuring series and/orparallel flows between neural networks as outputs which can becommunicated between such neural networks), a machine learning systemfor automatically configuring a topology or workflow for a set of hybridneural networks (e.g., series, parallel, data flows, etc.) based on atraining data set which may or may not use manual configurations (e.g.,by a human user), a deep learning system for automatically configuring atopology or workflow for a set of hybrid neural networks (e.g., series,parallel, data flows, etc.) based on a training data set of outcomesfrom industrial IoT processes (e.g., maintenance, repair, service,prediction of faults, optimization of operation of a machine, system offacility, etc.), or other intelligence or machine learning aspects.

Thus, in embodiments, the output of the wearable devices 14000 may beprocessed using the intelligent systems 14028 to add to, remove from, orotherwise modify the knowledge base 14036. For example, the knowledgebase 14036 may reflect information to use to perform one or more taskswithin the industrial environment in which the targets are located andin which the wearable devices 14000 are used. The output from thewearable devices 14000 can thus be used to increase knowledge as to thenature of issues that arise with respect to the industrial environment,for example, by describing information about the target from whichmeasurements were recorded, a time and/or date at which the measurementswere recorded, pre-existing state or other condition information aboutthe target, information about the time required to resolve an issue withrespect to a target, information about how to resolve an issue withrespect to a target, information indicating an amount of downtime to thetarget and to other aspects of the respective industrial environmentresulting from resolving the issue, an indication of whether the issueshould be resolved now or later (or not at all), and the like. Theintelligent systems 14028 may process that output to update existingtraining data. For example, the existing training data can be used toupdate the machine learning, artificial intelligence, and/or othercognitive functionality for identifying states of targets based on theoutput of the wearable devices 14000.

For example, the knowledge base 14036 may include a series of databasesor other tables or graphs arranged hierarchically based on the target orthe area of the industrial environment that includes the target. Forexample, a first layer of the knowledge base 14036 may refer to theindustrial environment (e.g., a power plant, a manufacturing facility, amining facility, etc.). A second layer of the knowledge base 14036 mayrefer to zones within the industrial environment (e.g., zone 1, zone 2,etc., or named zones, as the case may be). A third layer of theknowledge base 14036 may refer to targets within those zones (e.g.,within a first zone of a power plant including electrical equipment,this could include alternators, circuit breakers, transformers,batteries, exciters, etc., and, within a second zone of a power plantincluding a turbine, a generator, a generator magnet, etc.). Theknowledge base 14036 may be updated based on output of the intelligentsystems 14028, by manual user data entry, or both. For example, a workerwithin a power plant may be given one or more wearable devices (e.g.,the wearable devices 14000). In approaching a turbine, one of thewearable devices 14000 having a sensor for recording vibrationalmeasurements may determine that the turbine is vibrating at a particularrate. The output of the wearable device is processed by the intelligentsystems 14028, such as by comparing that output against the set of knowndata for the turbine. For example, the intelligent systems 14028 canquery data from the knowledge base 14036 indicating historicalmeasurements recorded with respect to the vibrations of that turbinewithin that particular power plant. The intelligent systems 14028 canthen determine whether the new output from the wearable device isconsistent with the data within the knowledge base 14036 or is devianttherefrom. In the event the new output deviates from the data within theknowledge base, the intelligent systems 14028 can update the data withinthat portion of the knowledge base 14036 to reflect the new output.Alternatively, the updating of the knowledge base 14036 may be delayed,for example, until after a threshold number of deviant outputmeasurements are recorded, so as to prevent misrepresentative outputfrom being used to modify the operational understanding of the turbine.

Disclosed herein are systems for data collection in an industrialenvironment with wearable device integration. As used herein, wearabledevice integration refers to using wearable devices for specific orgeneral purposes. For example, wearable device integration as describedwith respect to the functionality or configuration of a system refers tothe use by that system of the wearable devices 14000 and/or the hardwareand/or software used in connection with the wearable devices 14000 fordata collection within an industrial IoT environment, for example, asshown in FIGS. 286 to 289. Such wearable device integration refers tothe use of one or more of the wearable devices 14000. For example, asystem disclosed herein as including wearable device integration mayinclude integration of one or more of a shirt, vest, jacket, pair ofpants, pair of shorts, glove, sock, shoe, protective outerwear,undergarment, undershirt, tank top, hat, helmet, glasses, goggles,vision safety accessory, mask, chest band, belt, lift support garment,antenna, wrist band, ring, necklace, bracelets, watch, brooch, neckstrap, backpack, front pack, arm pack, leg pack, lanyard, key ring,headphones, hearing safety accessory, earbuds, or earpiece, or of othertypes of wearable devices or articles (e.g., articles of clothing and/oraccessory articles) including such other types of wearable devices.

In embodiments, a system for data collection in an industrialenvironment having the use of an analog cross point switch forcollecting variable groups of analog sensor inputs with wearable deviceintegration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having IP front signal conditioning on a multiplexer forimproved signal-to-noise ratio with wearable device integration isdisclosed.

In embodiments, a system for data collection in an industrialenvironment having multiplexer continuous monitoring alarming featureswith wearable device integration is disclosed.

In embodiments, system for data collection in an industrial environmenthaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections with wearabledevice integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having high-amperage input capability using solid staterelays and design topology with wearable device integration isdisclosed.

In embodiments, system for data collection in an industrial environmenthaving power-down ability of at least one of an analog sensor channeland a component board with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having unique electrostatic protection for trigger andvibration inputs with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having precise voltage reference for A/D zero reference withwearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a phase-lock loop band-pass tracking filter forobtaining slow-speed RPMs and phase information with wearable deviceintegration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having digital derivation of phase relative to input andtrigger channels using on-board timers with wearable device integrationis disclosed.

In embodiments, a system for data collection in an industrialenvironment having a peak-detector for auto-scaling that is routed intoa separate analog-to-digital converter for peak detection with wearabledevice integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having routing of a trigger channel that is either raw orbuffered into other analog channels with wearable device integration isdisclosed.

In embodiments, a system for data collection in an industrialenvironment having the use of higher input oversampling for delta-sigmaA/D for lower sampling rate outputs to minimize AA filter requirementswith wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling with wearable device integrationis disclosed.

In embodiments, a system for data collection in an industrialenvironment having long blocks of data at a high-sampling rate asopposed to multiple sets of data taken at different sampling rates withwearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having storage of calibration data with maintenance historyon-board card set with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a rapid route creation capability using hierarchicaltemplates with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having intelligent management of data collection bands withwearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a neural net expert system using intelligentmanagement of data collection bands with wearable device integration isdisclosed.

In embodiments, a system for data collection in an industrialenvironment having use of a database hierarchy in sensor data analysiswith wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having an expert system GUI graphical approach to definingintelligent data collection bands and diagnoses for the expert systemwith wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a graphical approach for back-calculation definitionwith wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having proposed bearing analysis methods with wearabledevice integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having torsional vibration detection/analysis utilizingtransitory signal analysis with wearable device integration isdisclosed.

In embodiments, a system for data collection in an industrialenvironment having improved integration using both analog and digitalmethods with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having adaptive scheduling techniques for continuousmonitoring of analog data in a local environment with wearable deviceintegration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having data acquisition parking features with wearabledevice integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a self-sufficient data acquisition box with wearabledevice integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having SD card storage with wearable device integration isdisclosed.

In embodiments, a system for data collection in an industrialenvironment having extended onboard statistical capabilities forcontinuous monitoring with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having the use of ambient, local and vibration noise forprediction with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having smart route changes route based on incoming data oralarms enable simultaneous dynamic data for analysis or correlation withwearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having smart ODS and transfer functions with wearable deviceintegration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having hierarchical multiplexer with wearable deviceintegration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having identification sensory overload with wearable deviceintegration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having RF identification and an inclinometer with wearabledevice integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having continuous ultrasonic monitoring with wearable deviceintegration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having cloud-based, machine pattern recognition based onfusion of remote, analog industrial sensors with wearable deviceintegration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having cloud-based, machine pattern analysis of stateinformation from multiple analog industrial sensors to provideanticipated state information for an industrial system with wearabledevice integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having cloud-based policy automation engine for IoT, withcreation, deployment and management of IoT devices with wearable deviceintegration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having on-device sensor fusion and data storage forindustrial IoT devices with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a self-organizing data marketplace for industrial IoTdata with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having self-organization of data pools based on utilizationand/or yield metrics with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having training AI models based on industry-specificfeedback with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a self-organized swarm of industrial data collectorswith wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having an IoT distributed ledger with wearable deviceintegration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a self-organizing collector with wearable deviceintegration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a network-sensitive collector with wearable deviceintegration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a remotely organized collector with wearable deviceintegration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a self-organizing storage for a multi-sensor datacollector with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a self-organizing network coding for multi-sensordata network with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a wearable haptic user interface for an industrialsensor data collector, with vibration, heat, electrical and/or soundoutputs with wearable device integration is disclosed.

In integrations, a system for data collection in an industrialenvironment having heat maps displaying collection data for AR/VR withwearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having automatically tuned AR/VR visualization of datacollected by a data collector with wearable device integration isdisclosed.

In embodiments, an intelligent cooking system having processing,communications, and other IT components for remote monitoring andcontrol with wearable device integration is disclosed.

In embodiments, an intelligent cooking system having a hydrogen fuelgenerating electrolyzer that operates on a water source to separatehydrogen and oxygen components with wearable device integration isdisclosed.

In embodiments, an intelligent cooking system having a low-pressurehydrogen storage system that stores the hydrogen generated by anelectrolyzer with wearable device integration is disclosed.

In embodiments, an intelligent cooking system having a fuel controlmodule that automatically controls fuel sourcing or mixing devices basedon some measure of historical, current, planned, and/or anticipatedconsumption or availability with wearable device integration isdisclosed.

In embodiments, an intelligent cooking system having a solar-poweredhydrogen electrolyzer with wearable device integration is disclosed.

In embodiments, an intelligent cooking system having a wind-poweredhydrogen electrolyzer with wearable device integration is disclosed.

In embodiments, an intelligent cooking system having a hydro-poweredhydrogen electrolyzer with wearable device integration is disclosed.

In embodiments, an intelligent cooking system having an on-demandgas-LPG hybrid burner that sources LPG, hydrogen, or other fueldynamically without need for user input or monitoring with wearabledevice integration is disclosed.

In embodiments, an intelligent cooking system having an enclosed burnerchamber that provides heat in a target heat-zone as a plane of heat withwearable device integration is disclosed.

In embodiments, an intelligent cooking system having a smart knob withconnectivity and local and remote control for controlling theintelligent cooktop device or other IoT devices with wearable deviceintegration is disclosed.

In embodiments, an intelligent cooking system having a mobile dockingfacility with power for charging a mobile device, data communications,and heat protection with wearable device integration is disclosed.

In embodiments, an intelligent cooking system having distributed modulesor components that are located in sub-systems of the cooktop withwearable device integration is disclosed.

In embodiments, an intelligent cooking system having a centralizedcontrol facility to manage operation of sub-systems of the cooktop withwearable device integration is disclosed.

In embodiments, an intelligent cooking system having remote controlcapability with wearable device integration is disclosed.

In embodiments, an intelligent cooking system having automation withwearable device integration is disclosed.

In embodiments, an intelligent cooking system having detectors andsensors for monitoring cooking system conditions with wearable deviceintegration is disclosed.

In embodiments, an intelligent cooking system having machine learningfor optimizing cooking system operation with wearable device integrationis disclosed.

In embodiments, an intelligent cooking system having a mobileapplication with wearable device integration is disclosed.

In embodiments, an intelligent cooking system having a cloud-basedplatform that interacts with electronic devices and participants in arelated ecosystem of suppliers, content providers, service providers,and regulators to deliver value-added services to users of theintelligent cooking system, users of the hydrogen production system, andother participants of the ecosystem with wearable device integration isdisclosed.

In embodiments, an intelligent cooking system having a recommendationengine for providing recommendations to users with wearable deviceintegration is disclosed.

In embodiments, an intelligent cooking system having a notificationengine for providing notifications to users with wearable deviceintegration is disclosed.

In embodiments, an intelligent cooking system having an advertisingengine for providing location-based offers to users with wearable deviceintegration is disclosed.

In embodiments, an intelligent cooking system having interfaces thatallow machine-to-machine or user-to-machine communication with otherdevices and the cloud, for contributing data for analytics, monitoring,control, and operation of other devices and systems with wearable deviceintegration is disclosed.

In embodiments, an intelligent cooking system having a user interfacethat facilitates contextual and intelligence-driven personalizedexperience for computing devices that connect to a network based aroundthe intelligent cooking system with wearable device integration isdisclosed.

In embodiments, an intelligent cooking system having analytics forprofiling, recording or analyzing users, usage of the device,maintenance and repair histories, patterns relating to patterns orfaults, energy use patterns, cooking patterns, and deployment, use, andservice of electrolyzer with wearable device integration is disclosed.

In embodiments, an intelligent cooking system having a commerce utilityfor ordering ingredients, components, and materials with wearable deviceintegration is disclosed.

In embodiments, an intelligent cooking system having a cookingassistance utility for assisting users with cooking tasks with wearabledevice integration is disclosed.

In embodiments, an intelligent cooking system having a health utilityfor providing health indices for foods, nutritional information,nutritional search capabilities, nutritional assistance, andpersonalized suggestions and recommendations with wearable deviceintegration is disclosed.

In embodiments, an intelligent cooking system having an infotainmentutility for playing music, videos, and/or podcasts with wearable deviceintegration is disclosed.

In embodiments, an intelligent cooking system having a broadcastingutility for enabling a personalized cooking channel that is broadcastfrom the cooking system with wearable device integration is disclosed.

In embodiments, an intelligent cooking system having a foodinvestigation utility for gathering information from smart cooktops anduser activity about recipes being used by users of the smart cooktopsystems throughout a region with wearable device integration isdisclosed.

In embodiments, a system for data collection in an industrialenvironment having an IoT platform with wearable device integration isdisclosed.

In embodiments, a system for data collection in an industrialenvironment having an IoT data adapter for receiving data inputs andestablishing a connection with one or more available IoT cloud platformsto publish the data with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a condition detector for detecting conditions relatedto connect attempts made by the IoT data adapter to one or more IoTcloud platforms with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having an IoT data adapter with an adaptation engine withwearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having the use of machine learning to prepare a data packetor stream with wearable device integration is disclosed.

In embodiments, a system for data collection in an industrialenvironment having a data marketplace that provides a pool of availablecloud networking platforms with wearable device integration isdisclosed.

In embodiments, a system for data collection in an industrialenvironment having a messaging utility that provides a cloud platformuser interface with a message indicating the availability of a new datasource and data source integration and usage instructions with wearabledevice integration is disclosed.

In embodiments, a system for data communication between nodes having theability to maintain a first and second transmission limit based onreceived rate of arrival and success of delivery feedback messages, andlimiting transmission of messages based on the transmission limits withwearable device integration is disclosed.

In embodiments, a system for data communication between nodes having theability to limit transmission of further messages not yet acknowledgedas successfully delivered according to the window size with wearabledevice integration is disclosed.

In embodiments, a system for data communication between nodes having theability to maintain an estimate of a rate of loss events and use it toadjust the rate of redundancy messages with wearable device integrationis disclosed.

In embodiments, a system for data communication between nodes having anestimated rate of loss events where the error correction code used todetermine redundancy messages chosen is based on the estimated rate ofloss events with wearable device integration is disclosed.

In embodiments, a system for data communication between nodes having theability to apply forward error correction based on messages receiveddescribing channel characteristics with wearable device integration isdisclosed.

In embodiments, a system for data communication between nodes having theability to maintain/set timers based on occurrences of delivery orderevents and delaying transmission of feedback messages using timers withwearable device integration is disclosed.

In embodiments, a system for data communication between nodes having theability to maintain/set timers based on occurrences of delivery orderevents and delaying modification of congestion window size based ontimers with wearable device integration is disclosed.

In embodiments, a system for data communication between nodes having theability to maintain/set timers based on occurrences of delivery orderevents, delaying modification of congestion window size based on timers,and cancelling modification of congestion window size when receiving afeedback message indicating successful delivery with wearable deviceintegration is disclosed.

In embodiments, a system for data communication between nodes having theability to configure a new connection using maintained datacharacterizing a current/previous connection with wearable deviceintegration is disclosed.

In embodiments, a system for data communication between nodes having theability to configure a new connection using maintained datacharacterizing an error rate of a current/previous connection withwearable device integration is disclosed.

In embodiments, a system for data communication between nodes having theability to configure a new connection using maintained datacharacterizing a timing variability of a current/previous connectionwith wearable device integration is disclosed.

In embodiments, a system for data communication between nodes having theability to configure a new connection using maintained datacharacterizing bandwidth of a current/previous connection with wearabledevice integration is disclosed.

In embodiments, a system for data communication between nodes having theability to configure a new connection using maintained datacharacterizing round trip time of a current/previous connection withwearable device integration is disclosed.

In embodiments, a system for data communication between nodes having theability to configure a new connection using maintained datacharacterizing communication control parameters of a current/previousconnection with wearable device integration is disclosed.

In embodiments, a system for data communication between nodes having theability to configure new connection using maintained data characterizingforward error correction parameters of a current/previous connectionwith wearable device integration is disclosed.

In embodiments, a system for data communication between nodes having adata store for maintaining data characterizing one or more current orprevious data communication connections and a connection initiationmodule for initiating new data communication connections based onmaintained data with wearable device integration is disclosed.

In embodiments, a system for data communication between nodes having theability to transmit a first subset of data messages over a lower latencydata path and a second subset of messages over a higher latency datapath with wearable device integration is disclosed.

In embodiments, a system for data communication between nodes having theability to transmit a first subset of data messages that are timecritical over a lower latency data path and a second subset of messagesover a higher latency data path with wearable device integration isdisclosed.

In embodiments, a system for data communication between nodes having theability to transmit a first initial subset of data messages over a lowerlatency data path and a second subset of messages that are subsequentlyavailable over a higher latency data path with wearable deviceintegration is disclosed.

In embodiments, a system for data communication between nodes having theability to transmit a first subset of acknowledgment messages over alower latency data path and a second subset of data messages over ahigher latency data path with wearable device integration is disclosed.

In embodiments, a system for data communication between nodes having theability to transmit a first subset of supplemental/redundancy datamessages over a lower latency data path and a second subset of datamessages over a higher latency data path with wearable deviceintegration is disclosed.

In embodiments, a system for data communication between nodes having adegree of redundancy associated with each message that is based onmessage position in the transmission order with wearable deviceintegration is disclosed.

In embodiments, a system for data communication between nodes having adegree of redundancy associated with each message that increases as theposition of the messages is non-decreasing with wearable deviceintegration is disclosed.

In embodiments, a system for data communication between nodes having adegree of redundancy associated with each message that is based onmessage position in the transmission order and in response to receivingfeedback messages with wearable device integration is disclosed.

In embodiments, a system for data communication between nodes having adegree of redundancy associated with each message that is based onmessage position in the transmission order and in response to receivingfeedback messages, and adding or removing redundancy messages from thequeue based on the feedback messages with wearable device integration isdisclosed.

In embodiments, a system for data communication between nodes having theability to adjust the number of messages sent over each of multipledifferent data paths with different communication protocols if it isdetermined that a data path is altering flow of messages with wearabledevice integration is disclosed.

In embodiments, a system for data communication between nodes having theability to adjust the number of messages sent over each of multipledifferent data paths with different communication protocols if it isdetermined that a data path is altering flow of messages initialdivision based on previous communication connections with wearabledevice integration is disclosed.

In embodiments, a system for data communication between nodes having theability to modify/add/remove redundancy information associated withencoded data as it travels from node to node via channels based onchannel characteristics with wearable device integration is disclosed.

In embodiments, a system for data communication between nodes having theability to send FEC packets at an estimated rate of loss events(isolated packet loss or burst of consecutive packets) with wearabledevice integration is disclosed.

In embodiments, a system for data communication between nodes havingcombined coding, TCP, and pacing of packet transmissions with wearabledevice integration is disclosed.

In embodiments, a system for data communication between nodes having aforward error correction code construction that interleaves groups ofmessage packets and parity packets and has coding across overlappinggroups of message packets with wearable device integration is disclosed.

In embodiments, a system for data communication between nodes having avariant of TCP that combines delay-based back-off with a stable windowincrease function with wearable device integration is disclosed.

Systems and methods for using mobile robots and/or mobile vehicles formobile data collection within an environment for industrial IoT datacollection are next described with respect to FIGS. 290 to 292.Referring first to FIG. 290, a data collection system may include one ormore mobile robots and/or mobile vehicles configured to act as mobiledata collectors within an environment for industrial IoT datacollection. For example, the one or more mobile robots and/or mobilevehicles may transmit data to, receive data from, transmit commands to,receive commands from, be under the control of, communicate controlsfor, or otherwise communicate with the industrial IoT data collection,monitoring and control system 10. Methods and systems are disclosedherein for data collection using mobile robots and/or mobile vehicles,including a mobile robot with one or more mobile data collectorsintegrated therein, a mobile vehicle with one or more mobile datacollectors integrated therein, a mobile robot with one or more mobiledata collectors coupled thereto, and a mobile vehicle with one or moremobile data collectors coupled thereto. As used herein, the term “mobilerobot” may refer to, but is not limited to, a robotic arm, androidrobot, small or large autonomous robot, remote-controlled robot,programmably configured robot, or other robotic mechanism. Examples ofmobile robots within which a mobile data collector may be integrated orto which a mobile data collector may be coupled include, withoutlimitation, any of the foregoing types of mobile robot. As used herein,the term “mobile vehicle” may refer to, but is not limited to, aheavy-duty machine (e.g., earthmoving equipment), heavy-duty on-roadindustrial vehicle, heavy-duty off-road industrial vehicle, industrialmachine deployed in various settings (e.g., turbines, turbomachinery,generators, pumps, pulley systems, manifold, valve systems, and thelike), earth-moving equipment, earth-compacting equipment, haulingequipment, hoisting equipment, conveying equipment, aggregate productionequipment, equipment used in concrete construction, piledrivingequipment, construction equipment (e.g., excavators, backhoes, loaders,bulldozers, skid steer loaders, trenchers, motor graders, motorscrapers, crawler loaders, wheeled loading shovels, dumpers, tankers,tippers, trailers, tunnel and handling equipment, road rollers, concretemixers, hot mix plants, road making machines (e.g., compactors), stonecrashers, pavers, slurry seal machines, spraying and plasteringmachines, heavy-duty pumps, and the like), material handling equipment(e.g., cranes, conveyors, forklift, hoists, and the like), personneltransport vehicles (e.g., cars, trucks, carts, watercraft, aircraft, andthe like), unmanned vehicles (e.g., drones or other autonomous aircraft,autonomous watercraft, autonomous cars or trucks, and the like), othervehicles (e.g., regardless of size, purpose, or use of a motor), and thelike. Examples of mobile vehicles within which a mobile data collectormay be integrated or to which a mobile data collector may be coupledinclude, without limitation, any suitable mobile vehicle. Regardless ofthe particular form, a mobile robot or mobile vehicle according to thisdisclosure includes one or more mobile data collectors, which are orinclude sensors for recording state-related measurements of anenvironment for industrial IoT data collection. For example, the one ormore sensors of a mobile data collector described in this disclosure canmeasure states with respect to equipment within an industrial IoTenvironment or with respect to the industrial IoT environment itself.Examples of mobile data collectors which may be integrated within and/orcoupled to a mobile robot or a mobile vehicle include, withoutlimitation, a mobile phone, a laptop computer, a tablet computer, apersonal digital assistant, a walkie-talkie, a radio, a long or shortrange communication device, a flashlight, and the like. The sensors of amobile data collector integrated within and/or coupled to a mobile robotor a mobile vehicle may measure one or more of vibrations, temperatures,electrical output, magnetic output, sound output, or other output of orotherwise relating to a target within the industrial IoT environment.

In embodiments, a mobile data collector swarm 14038 includes a number ofmobile robots and/or mobile vehicles. The mobile robots and/or mobilevehicles of the swarm 14038 may be mobile robots and/or mobile vehiclesnative to the industrial IoT environment or mobile robots and/or mobilevehicles brought into the industrial IoT environment from a differentlocation. As shown in FIG. 290, the swarm 14038 may include differenttypes of mobile robots and/or mobile vehicles, including a mobile robotwith one or more mobile data collectors integrated therein 14040, amobile vehicle with one or more mobile data collectors integratedtherein 14042, a mobile robot with one or more mobile data collectorscoupled thereto 14044, and a mobile vehicle with one or more mobile datacollectors coupled thereto 14046. In embodiments, a mobile datacollector is integrated within a mobile robot or mobile vehicle whenremoval of the mobile data collector from the mobile robot or mobilevehicle during the typical operation of the mobile robot or mobilevehicle would result in disruption to the principle operation of themobile robot or mobile vehicle. In embodiments, a mobile data collectoris coupled to a mobile robot or mobile vehicle when the mobile datacollector is able to be removed or otherwise uncoupled from the mobilerobot or mobile vehicle without material disruption to the principleoperation of the mobile robot or mobile vehicle.

The mobile robots and mobile vehicles of the mobile data collector swarm14038 collect data from targets 14048 (e.g., the targets 12002 shown inFIG. 164, or any other suitable target). In embodiments, data collectedby the mobile data collectors from the targets 14048 can be stored in adata pool 14050 (e.g., the data pool 14012 shown in FIG. 286, or anyother suitable data pool). For example, the targets 14048 may be orinclude one or more of machines, pipelines, equipment, installations,tools, vehicles, turbines, speakers, lasers, automatons, computerequipment, industrial equipment, switches, and the like.

Different mobile robots and/or mobile vehicles of the swarm 14038 may beconfigured to record certain types of state-related measurements of someor all of the targets 14048. For example, some of the mobile robotsand/or the mobile vehicles of the swarm 14038 may be configured torecord state-related measurements based on vibrations measured withrespect to some or all of the targets 14048. In another example, some ofthe mobile robots and/or the mobile vehicles of the swarm 14038 may beconfigured to record state-related measurements based on temperaturesmeasured with respect to some or all of the targets 14048. In anotherexample, some of the mobile robots and/or the mobile vehicles of theswarm 14038 may be configured to record state-related measurements basedon electrical or magnetic outputs measured with respect to some or allof the targets 14048. In another example, some of the mobile robotsand/or the mobile vehicles of the swarm 14038 may be configured torecord state-related measurements based on sound outputs measured withrespect to some or all of the targets 14048. In another example, some ofthe mobile robots and/or the mobile vehicles of the swarm 14038 may beconfigured to record state-related measurements based on outputs otherthan vibrations, temperatures, electrical or magnetic, or sound, asmeasured with respect to some or all of the targets 14048.

Alternatively, or additionally, different mobile robots and/or mobilevehicles of the swarm 14038 may be configured to record some or allstate-related measurements of certain types of the targets 14048. Forexample, some of the mobile robots and/or the mobile vehicles of theswarm 14038 may be configured to record some or all state-relatedmeasurements from agitators (e.g., turbine agitators), airframe controlsurface vibration devices, catalytic reactors, compressors, and thelike. In another example, some of the mobile robots and/or the mobilevehicles of the swarm 14038 may be configured to record some or allstate-related measurements from conveyors and lifters, disposal systems,drive trains, fans, irrigation systems, motors, and the like. In anotherexample, some of the mobile robots and/or the mobile vehicles of theswarm 14038 may be configured to record some or all state-relatedmeasurements from pipelines, electric powertrains, production platforms,pumps (e.g., water pumps), robotic assembly systems, thermic heatingsystems, tracks, transmission systems, turbines, and the like. Inembodiments, the mobile robots and/or the mobile vehicles of the swarm14038 may be configured to record some or all state-related measurementsof certain types of industrial environments. For example, an industrialenvironment having targets with states measured using the mobile robotsand/or the mobile vehicles of the swarm 14038 may include, but is notlimited to, a manufacturing environment, a fossil fuel energy productionenvironment, an aerospace environment, a mining environment, aconstruction environment, a ship environment, a shipping environment, asubmarine environment, a wind energy production environment, ahydroelectric energy production environment, a nuclear energy productionenvironment, an oil drilling environment, an oil pipeline environment,any other suitable energy product environment, any other suitable energyrouting or transmission environment, any other suitable industrialenvironment, a factory, an airplane or other aircraft, a distributionenvironment, an energy source extraction environment, an offshoreexploration site, an underwater exploration site, an assembly line, awarehouse, a power generation environment, a hazardous wasteenvironment, and the like.

The swarm 14038 includes self-organization systems 14052 for causing themobile robots or mobile vehicles within the swarm 14038 to self-organize(e.g., during data collection operations within the industrial IoTenvironment). In embodiments, a data collection system that includes theswarm 14038 (e.g., the data collection system 12004 or any othersuitable data collection system) may include self-organizationfunctionality, which can be performed at or by any of the components ofthe data collection system. In embodiments, a mobile robot or mobilevehicle of the swarm 14038 can self-organize without assistance fromother components and based on, for example, the data sensed by itsassociated sensors and other knowledge. In embodiments, the network14010 can be accessed for the self-organization without assistance fromother components and based on, for example, the data sensed by themobile robots and/or mobile vehicles, or other knowledge. It should beappreciated that any combination or hybrid-type self-organization systemcan also be embodied. For example, the data collection system canperform or enable various methods or systems for data collection havingself-organization functionality in an industrial IoT environment. Thesemethods and systems can include analyzing a plurality of sensor inputs,for example, received from or sensed by sensors at the mobile robotsand/or at the mobile vehicles of the swarm 14038. The methods andsystems can also include sampling the received data and self-organizingat least one of: (i) a storage operation of the data (e.g., with respectto the data pool 14050); (ii) a collection operation of sensors thatprovide the plurality of sensor inputs, and (iii) a selection operationof the plurality of sensor inputs.

In embodiments, the self-organization systems 14052 can be used tocollectively organize two or more of the mobile robots and/or the mobilevehicles of the swarm 14038. Alternatively, the self-organizationsystems 14052 can be used to organize individual mobile robots and/orthe mobile vehicles of the swarm 14038. For example, theself-organization systems 14052 can control the traversal of each of themobile robots and each of the mobile vehicles of the swarm 14038 withindifferent regions, sections, or other divided areas of the industrialIoT environment. In embodiments, there may be other mobile robots withone or more mobile data collectors integrated therein, other mobilevehicles with one or more mobile data collectors integrated therein,other mobile robots with one or more mobile data collectors coupledthereto, and/or other mobile vehicles with one or more mobile datacollectors coupled thereto, which collect data for some or all of thetargets 14048, but which are not included in the swarm 14038. Such othermobile robots and/or other mobile vehicles may be controlledindividually (e.g., outside of the self-organization systems 14052).

In embodiments, the swarm 14038 may include intelligent systems 14054that process the state-related measurements recorded using the mobilerobots and/or the mobile vehicles of the swarm 14038 before transmittingthose recorded state-related measurements over the network 14010 or anyother suitable communication mechanism. For example, some or all of themobile robots and/or the mobile vehicles of the swarm 14038 mayintegrate artificial intelligence processes, machine learning processes,and/or other cognitive processes for analyzing the state-relatedmeasurements recorded thereby. In embodiments, the processing by theintelligent systems 14054 of the mobile robots and/or the mobilevehicles of the swarm 14038 may be or be represented within apre-processing step of the industrial IoT data collection, monitoringand control system 10. For example, certain types of the mobile robotsand/or the mobile vehicles of the swarm 14038 may selectively performpre-processing of the recorded state-related measurements to identifyredundant information, irrelevant information, or insignificantinformation. In another example, certain types of the mobile robotsand/or the mobile vehicles of the swarm 14038 may pre-process therecorded state-related measurements in an automated manner, so as toidentify redundant information, irrelevant information, or insignificantinformation. In another example, the pre-processing may be selectivelyperformed for certain types of state-related measurements recorded byany of the mobile robots and/or the mobile vehicles of the swarm 14038to pre-process the recorded state-related measurements (e.g., toidentify redundant information, irrelevant information, or insignificantinformation). In another example, the pre-processing may be automatedfor certain types of state-related measurements recorded by any of themobile robots and/or the mobile vehicles of the swarm 14038 topre-process the recorded state-related measurements (e.g., to identifyredundant information, irrelevant information, or insignificantinformation).

In embodiments, the state-related measurements recorded using the mobilerobots and/or the mobile vehicles of the swarm 14038 may be madeavailable over the network 14010 (e.g., as described with respect toFIG. 286) without the need for external networks. The network 14010 maybe a MANET (e.g., the MANET 20 shown in FIG. 2 or any other suitableMANET), the Internet (e.g., the Internet 110 shown in FIG. 3 or anyother suitable Internet), or any other suitable type of network, or anycombination thereof. For example, the network 14010 may be used toreceive state-related measurements recorded using the mobile robotsand/or the mobile vehicles of the swarm 14038. The network 14010 maythen be used to transmit some or all of those received state-relatedmeasurements to other components of the data collection system 102. Forexample, the network 14010 may be used to transmit some or all of thereceived state-related measurements to the data pool 14050 (e.g., thedata pool 60 shown in FIG. 2 or any other suitable data pool) forstorage of those received state-related measurements. In anotherexample, the network 14010 may be used to transmit some or all of thereceived state-related measurements to servers 14056 of the environmentfor industrial IoT data collection (e.g., the servers 14014 shown inFIG. 286, or any other suitable server). The servers 14056 may includeone or more hardware or software server aspects. For example, theservers 14056 to which the received state-related measurements aretransmitted may include intelligent systems 14058 for processing thereceived state-related measurements. The intelligent systems 14058 mayprocess the received state-related measurements using artificialintelligence processes, machine learning processes, and/or othercognitive processes to identify information within or otherwiseassociated with the received state-related measurements. In embodiments,after processing the received state-related measurements, the servers14056 to which the received state-related measurements are transmittedmay transmit the processed information or data indicative of theprocessed information to other systems (e.g., for storage or analysis).In embodiments, the data indicative of the processed information fromthe servers 14056 may include, for example, output or other results ofthe artificial intelligence processes, machine learning processes,and/or other cognitive processes.

In embodiments, a mobile robot or a mobile vehicle of the swarm 14038may include a computer vision system or otherwise include computervision functionality. For example, computer vision functionality of themobile robot or of the mobile vehicle can include hardware and softwareconfigured to identify objects in a multi-axial space using imagesensing. In embodiments, the computer vision functionality within themobile robot or within the mobile vehicle can include functionality forobserving visible states of the targets 14048 during the normaloperation of the mobile robot or the mobile vehicle. In embodiments,data processed by the computer vision functionality of the mobile robotor of the mobile vehicle can be input to the intelligent systems 14054(e.g., for further processing and learning of the targets 14048 and/orof the environment that includes the targets 14048).

In embodiments, some or all of the mobile robots and/or the mobilevehicles of the swarm 14038 may include sensor fusion functionality. Forexample, the sensor fusion functionality may be embodied as theon-device sensor fusion 80. For example, state-related measurementsrecorded using multiple analog sensors of one or more of the mobilerobots and/or the mobile vehicles of the swarm 14038 (e.g., the multipleanalog sensors 82 shown in FIG. 4 or any other suitable sensor) may belocally or remotely processed using artificial intelligence processes,machine learning processes, and/or other cognitive processes, which maybe embodied within the mobile robots and/or the mobile vehicles of theswarm 14038 themselves, the servers 14056, or both. In embodiments, thesensor fusion functionality may be embodied by a pre-processing stepthat is performed prior to the artificial intelligence processes,machine learning processes, and/or other cognitive processes. Inembodiments, the sensor fusion functionality may be performed using aMUX. For example, each of the mobile robots and/or the mobile vehiclesof the swarm 14038 may include its own MUX for combining state-relatedmeasurements recorded using individual sensors of those multiplesensors. In some such embodiments, the MUX may be internal to the mobilerobots and/or the mobile vehicles of the swarm 14038. In some suchembodiments, the MUX may be external to the mobile robots and/or themobile vehicles of the swarm 14038.

In embodiments, the state-related measurements recorded using the mobilerobots and/or the mobile vehicles of the swarm 14038 may be pulled fromthe mobile robots and/or mobile vehicles by an upstream device (e.g., aclient device or other software or hardware aspect used to review,analyze, or otherwise view the state-related measurements). For example,the mobile robots and/or the mobile vehicles of the swarm 14038 may notactively transmit the state-related measurements that are received(e.g., at the servers 14056, the data pool 14050, or any other suitablehardware or software component that receives the state-relatedmeasurements recorded using the mobile robots and/or the mobile vehiclesof the swarm 14038). Rather, the transmission of the state-relatedmeasurements from the mobile robots and/or the mobile vehicles of theswarm 14038 may be caused by commands received at the mobile robotsand/or the mobile vehicles of the swarm 14038 (e.g., from servers 14056or from other hardware or software of the data collection system 102).For example, a data collector of any of the mobile robots and/or themobile vehicles of the swarm 14038 may be configured to pullstate-related measurements recorded using that mobile robot or mobilevehicle. For example, the mobile robots and/or the mobile vehicles ofthe swarm 14038 may continuously, periodically, or otherwise at multipletimes record state-related measurements within the environment forindustrial IoT data collection. The data collector may, at fixedintervals, at random times, or otherwise, transmit one or more commandsto some or all of the mobile robots and/or the mobile vehicles of theswarm 14038, for example, to pull some or all of the state-relatedmeasurements recorded using the mobile robots and/or the mobile vehiclesof the swarm 14038 since the last time state-related measurements werepulled therefrom. In another example, the commands, when processed usingindividual mobile robots and/or the mobile vehicles of the swarm 14038,cause the recorded state-related measurements or data representativethereof to be transmitted from the mobile robots and/or the mobilevehicles of the swarm 14038.

In embodiments, the state-related measurements recorded using the mobilerobots and/or the mobile vehicles of the swarm 14038 may be transmittedfrom the mobile robots and/or the mobile vehicles of the swarm 14038responsive to requests for those state-related measurements. Forexample, the self-organization systems 14052 may, at fixed intervals, atrandom times, or otherwise, transmit a request for recordedstate-related measurements to some or all of the mobile robots and/orthe mobile vehicles of the swarm 14038. The processors of some or all ofthe mobile robots and/or the mobile vehicles of the swarm 14038 to whichthe request is sent may process the request to determine whichstate-related measurements to transmit. For example, data indicative ofa time of a most recent request for recorded state-related measurementsmay be accessed by those processors. The processors may then comparethat time to a time at which the new request is received from theself-organization systems 14052. The processors may then query a datastore for state-related measurements recorded between the two times. Theprocessors may then transmit those state-related measurements inresponse to the request. In another example, the processors may identifya most recent set of state-related measurements recorded using thecorresponding mobile robots and/or the mobile vehicles of the swarm14038 and transmit those state-related measurements in response to therequest. In another example, data collectors within the data collectionsystem 10 may transmit the request directly to the mobile robots and/orthe mobile vehicles of the swarm 14038. In yet another example, themobile robots and/or the mobile vehicles of the swarm 14038 may transmitthe request to the self-organization systems 14052. Theself-organization systems 14052 may process the request to determineselect individual mobile robots and/or the mobile vehicles of the swarm14038 which were used to record the requested state-relatedmeasurements. In embodiments, the collective processing mind 14020 maythen transmit certain state-related measurements in response to therequest by, for example, querying a storage for some or all of thestate-related measurements recorded using those select individual mobilerobots and/or the mobile vehicles of the swarm 14038. Alternatively, theself-organization systems 14052 may process the request to determinewhich of the state-related measurements recorded by some or all of themobile robots and/or the mobile vehicles of the swarm 14038 to transmitin response to the request (e.g., based on a time of the request). Forexample, the self-organization systems 14052 can compare the time of therequest to a time of a most recent request for recorded state-relatedmeasurements. The self-organization systems 14052 can then retrieve thestate-related measurements recorded in between those times and transmitthe retrieved state-related measurements in response to the request.

In embodiments, the state-related measurements recorded using the mobilerobots and/or the mobile vehicles of the swarm 14038 may be pushed to anupstream device (e.g., a client device or other software or hardwareaspect used to review, analyze, or otherwise view the state-relatedmeasurements). For example, the mobile robots and/or the mobile vehiclesof the swarm 14038 may actively transmit the state-related measurementsthat are received (e.g., at the servers 14056, the data pool 14050, orany other suitable hardware or software component that receives thestate-related measurements recorded using the mobile robots and/or themobile vehicles of the swarm 14038), without such receiving hardware orsoftware component requesting those state-related measurements orotherwise causing the mobile robot or the mobile vehicle to transmitthose state-related measurements based on a command. For example, someor all of the mobile robots and/or the mobile vehicles of the swarm14038 may transmit state-related measurements on a fixed interval, atrandom times, immediately upon the recording of those state-relatedmeasurements, some amount of time after recording those measurements,upon a determination that a threshold number of state-relatedmeasurements have been recorded, or at other suitable times. In somesuch embodiments, the mobile robots and/or the mobile vehicles of theswarm 14038, either by themselves or using the self-organization systems14052, may push the recorded state-related measurements in response todetecting a near proximity of a data collection router 14062.

For example, referring next to FIG. 291, upon the detection of thetarget 14048 by a mobile robot or mobile vehicle 14060 (e.g., one ormore of the mobile robot with one or more mobile data collectorsintegrated therein 14040, the mobile vehicle with one or more mobiledata collectors integrated therein 14042, the mobile robot with one ormore mobile data collectors coupled thereto 14044, or the mobile vehiclewith one or more of the mobile data collectors coupled thereto 14046 ofthe swarm 14038), the mobile robot or mobile vehicle 14060 recordsstate-related measurements of the target 14048 (e.g., vibrations,temperature, electrical or magnetic output, sound output, or the like).The recorded state-related measurements can be transmitted over thenetwork 14010 (e.g., to the data pool 14050, the servers 14056, oranother hardware or software component). Alternatively, the recordedstate-related measurements can be transmitted to the data collectionrouter 14062, for example, where the network 14010 is unavailable orwhere the data collection router 14062 is configured to receive and/orpre-process the recorded state-related measurements from the mobilerobot or mobile vehicle 14060. The data collection router 14062 may beone of a number of data collection routers 14062 located throughout theenvironment for industrial IoT data collection. For example, the datacollection router 14062 may be a data collection router 14062 configuredto transmit state-related measurements specifically recorded for thetarget 14048.

Referring next to FIG. 292, various aspects of functionality ofintelligent systems 14064 used to process output of the mobile robotsand/or the mobile vehicles of the swarm 14038 are disclosed. Inembodiments, the intelligent systems 14064 may include a cognitivelearning module 14066, an artificial intelligence module 14068, and amachine learning module 14070. The intelligent systems 14064 may includeadditional or fewer modules. The intelligent systems 14064 may, forexample, be the intelligent systems 14054 or the intelligent systems14058 shown in FIG. 290 or any other suitable intelligent systems.Although shown as separate modules, in embodiments, there may be overlapbetween some or all of the cognitive learning module 14066, theartificial intelligence module 14068, and the machine learning module14070. For example, the artificial intelligence module 14068 may includethe machine learning module 14070. In another example, the cognitivelearning module 14066 may include the artificial intelligence module14068 (and, in embodiments, therefore, the machine learning module14070). The swarm 14038 may include any number of mobile robots and/ormobile vehicles. For example, as shown, the swarm 14038 includes a firstmobile robot or first mobile vehicle 14060A, a second mobile robot orsecond mobile vehicle 14060B, and an Nth mobile robot or Nth mobilevehicle 14060N, where N is a number greater than two. The intelligentsystems 14064 receives the output of the mobile robots or mobilevehicles 14060A, 14060B, . . . 14060N. In particular, one or more of themodules 14066, 14068, and 14070 of the intelligent systems 14064receives data generated by and output from one or more of the mobilerobots or mobile vehicles 14060A, 14060B, . . . 14060N. The output fromthe mobile robots or mobile vehicles 14060A, 14060B, . . . 14060N may,for example, include state-related measurements recorded using themobile robots or mobile vehicles 14060A, 14060B, . . . 14060N, (e.g.,state-related measurements of equipment within an environment forindustrial IoT data collection). In embodiments, the output from themobile robots or mobile vehicles 14060A, 14060B, . . . 14060N may beprocessed by all three of the modules 14066, 14068, and 14070 of theintelligent systems 14064. In embodiments, the output from the mobilerobots or mobile vehicles 14060A, 14060B, . . . 14060N may be processedby only one of the modules 14066, 14068, and 14070 of the intelligentsystems 14064. For example, the particular one of the modules 14066,14068, and 14070 of the intelligent systems 14064 to use to process theoutput from the mobile robots or mobile vehicles 14060A, 14060B, . . .14060N may be selected based on the mobile robot and/or mobile vehicleused to generate that output, the equipment measured in generating thatoutput, the values of the output, other selection criteria, and thelike.

The knowledge base 14036 (e.g., as described with respect to FIG. 289)may be updated based on output from the intelligent systems 14064. Theknowledge base 14036 represents a library or other set or collection ofknowledge related to the environment of the industrial IoT datacollection, including equipment within that environment, tasks performedwithin that environment, personnel having the skill to perform taskswithin that environment, and the like. The intelligent systems 14064 canprocess the state-related measurements recorded using the mobile robotsor mobile vehicles 14060A, 14060B, . . . 14060N to facilitate knowledgegathering for expanding the knowledge base 14036. For example, themodules 14066, 14068, and 14070 of the intelligent systems 14064 canprocess those state-related measurements against existing knowledgewithin the knowledge base 14036 to update or otherwise modifyinformation within the knowledge base 14036. The intelligent systems14064 may use intelligence and machine learning capabilities (e.g., ofthe machine learning module 14070 or as described elsewhere in thisdisclosure) to process state-related measurements and relatedinformation based on detected conditions (e.g., conditions informed bythe mobile robots and/or mobile vehicles of the swarm 14038 and/orprovided as training data) and/or state information (e.g., stateinformation determined by a machine state recognition system that maydetermine a state, for example, relating to an operational state, anenvironmental state, a state within a known process or workflow, a stateinvolving a fault or diagnostic condition, and the like). This mayinclude optimization of input selection and configuration based onlearning feedback from the learning feedback system, which may includeproviding training data (e.g., from a host processing system or fromother data collection systems either directly or from the hostprocessing system) and may include providing feedback metrics (e.g.,success metrics calculated within an analytic system of the hostprocessing system). Examples of learning feedback systems, datacollection systems, and analytic systems are described elsewhere in thisdisclosure. Thus, the intelligent systems 14064 can be used to updateworkflows of tasks assigned and performed within the industrial IoTenvironment based on output from the mobile robots or mobile vehicles14060A, 14060B, . . . 14060N.

In embodiments, the intelligent systems 14064, either within one of themodules 14066, 14068, and 14070 or otherwise, may include otherintelligence or machine learning aspects. For example, the intelligentsystems 14064 may include one or more of a YOLO neural network, a YOLOCNN, a set of neural networks configured to operate on or from a FPGA, aset of neural networks configured to operate on or from a FPGA and GPUhybrid component, a user configurable series and parallel flow for ahybrid neural network (e.g., configuring series and/or parallel flowsbetween neural networks as outputs which can be communicated betweensuch neural networks), a machine learning system for automaticallyconfiguring a topology or workflow for a set of hybrid neural networks(e.g., series, parallel, data flows, etc.) based on a training data setwhich may or may not use manual configurations (e.g., by a human user),a deep learning system for automatically configuring a topology orworkflow for a set of hybrid neural networks (e.g., series, parallel,data flows, etc.) based on a training data set of outcomes fromindustrial IoT processes (e.g., maintenance, repair, service, predictionof faults, optimization of operation of a machine, system of facility,etc.), or other intelligence or machine learning aspects.

Thus, in embodiments, the output of the mobile robots and/or mobilevehicles of the swarm 14038 may be processed using the intelligentsystems 14054 to add to, remove from, or otherwise modify the knowledgebase 14036. For example, the knowledge base 14036 may reflectinformation to use to perform one or more tasks within the industrialenvironment in which the targets are located and in which the mobilerobots and/or mobile vehicles of the swarm 14038 are used. The outputfrom the mobile robots and/or mobile vehicles of the swarm 14038 canthus be used to increase knowledge as to the nature of issues that arisewith respect to the industrial environment, for example, by describinginformation about the target from which measurements were recorded, atime and/or date at which the measurements were recorded, pre-existingstate or other condition information about the target, information aboutthe time required to resolve an issue with respect to a target,information about how to resolve an issue with respect to a target,information indicating an amount of downtime to the target and to otheraspects of the respective industrial environment resulting fromresolving the issue, an indication of whether the issue should beresolved now or later (or not at all), and the like. The intelligentsystems 14054 may process that output to update existing training data.For example, the existing training data can be used to update themachine learning, artificial intelligence, and/or other cognitivefunctionality for identifying states of targets based on the output ofthe mobile robots and/or mobile vehicles of the swarm 14038.

For example, the knowledge base 14036 may include a series of databasesor other tables or graphs arranged hierarchically based on the target orthe area of the industrial environment that includes the target. Forexample, a first layer of the knowledge base 14036 may refer to theindustrial environment (e.g., a power plant, a manufacturing facility, amining facility, etc.). A second layer of the knowledge base 14036 mayrefer to zones within the industrial environment (e.g., zone 1, zone 2,etc., or named zones, as the case may be). A third layer of theknowledge base 14036 may refer to targets within those zones (e.g.,within a first zone of a power plant including electrical equipment,this could include alternators, circuit breakers, transformers,batteries, exciters, etc., and, within a second zone of a power plantincluding a turbine, a generator, a generator magnet, etc.). Theknowledge base 14036 may be updated based on output of the intelligentsystems 14054, by manual user data entry, or both.

For example, the mobile robots and/or mobile vehicles of the swarm 14038may be deployed to monitor or otherwise traverse different locations(e.g., zones) within a mining facility used to mine and/or process fuelmaterials (e.g., coal, natural gas, etc.) and/or non-fuel materials(e.g., stone, sand, gravel, gold, silver, etc.). A mobile robot may bedeployed to traverse a first zone in which mineral crushing machinery isoperating, and a mobile vehicle may be deployed to traverse a secondzone in which underground mining equipment is operating. The mobilerobot may measure the operating temperatures of the mineral crushingmachinery within the first zone, the temperature of areas of the firstzone around the mineral crushing machinery, and the like. The mobilerobot may further measure the sound output from the mineral crushingmachinery, for example, by recording measurements of the sound outputfrom some or all of the machinery. The mobile robot can detect anoverheating issue with respect to one of the mineral crushing machinesif it records a temperature measurement which, when processed by theintelligent systems 14054 against the data stored in the knowledge base14036, indicates that the temperature is at a dangerous level. Themobile robot may be instructed to remain at the location of that machineand record new temperature measurements over some period of time (e.g.,at fixed intervals or otherwise) to determine whether the machine isactually operating at a dangerously high temperature. If the intelligentsystems 14054 detects that the initial high temperature measurement wasnot representative of the operating temperature of the machine, theintelligent systems 14054 may either not update the knowledge base 14036to reflect the misrepresentative measurement or instead may update theknowledge base 14036 to reflect that such a temperature reading may notrepresent a dangerous condition.

The mobile vehicle may measure vibrational output with respect to theunderground mining equipment. The output of the mobile vehicle may beprocessed using the intelligent systems 14054 to determine whether it isconsistent with the data within the knowledge base 14036 or is devianttherefrom. In the event the output of the mobile vehicle deviates fromthe data within the knowledge base, the intelligent systems 14054 canupdate the data within that portion of the knowledge base 14036 toreflect the output of the mobile vehicle. The intelligent systems 14054may also or instead cause the mobile vehicle to emit an alarm (e.g.,using lights, sounds, or both) to warn personnel located in that zone.For example, the intelligent systems 14054 may retrieve information fromthe knowledge base 14036 suggesting that the output of the mobilevehicle reflects a dangerous condition, for example, related to apotential underground cave-in. In some scenarios, the intelligentsystems 14054 may transmit a notification directly to an operator of theunderground machinery to alert them to the dangerous condition.

Disclosed herein are systems for using a mobile robot and/or a mobilevehicle for data collection in an industrial environment. As usedherein, using a mobile robot and/or a mobile vehicle refers to using amobile robot and/or a mobile vehicle for specific or general purposes.For example, using a mobile robot and/or a mobile vehicle as describedwith respect to the functionality or configuration of a system refers tothe use by that system of the mobile robots and/or mobile vehicles ofthe swarm 14038 and/or the hardware and/or software used in connectionwith the mobile robots and/or mobile vehicles of the swarm 14038 fordata collection within an industrial IoT environment, as shown in FIGS.290 to 292. Such use of a mobile robot and/or a mobile vehicle refers tothe use of one or more of the mobile robots and/or mobile vehicles ofthe swarm 14038. For example, a system disclosed herein as using amobile robot and/or a mobile vehicle may use one or more of a roboticarm, android robot, small or large autonomous robot, remote-controlledrobot, programmably configured robot, other robotic mechanism,heavy-duty machine (e.g., earthmoving equipment), heavy-duty on-roadindustrial vehicle, heavy-duty off-road industrial vehicle, industrialmachine deployed in various settings (e.g., turbines, turbomachinery,generators, pumps, pulley systems, manifold, valve systems, and thelike), earth-moving equipment, earth-compacting equipment, haulingequipment, hoisting equipment, conveying equipment, aggregate productionequipment, equipment used in concrete construction, piledrivingequipment, construction equipment (e.g., excavators, backhoes, loaders,bulldozers, skid steer loaders, trenchers, motor graders, motorscrapers, crawler loaders, wheeled loading shovels, dumpers, tankers,tippers, trailers, tunnel and handling equipment, road rollers, concretemixers, hot mix plants, road making machines (e.g., compactors), stonecrashers, pavers, slurry seal machines, spraying and plasteringmachines, heavy-duty pumps, and the like), material handling equipment(e.g., cranes, conveyors, forklift, hoists, and the like), personneltransport vehicles (e.g., cars, trucks, carts, watercraft, aircraft, andthe like), unmanned vehicles (e.g., drones or other autonomous aircraft,autonomous watercraft, autonomous cars or trucks, and the like), othervehicles (e.g., regardless of size, purpose, or use of a motor), and thelike.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having the useof an analog cross point switch for collecting variable groups of analogsensor inputs with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having IP frontsignal conditioning on a multiplexer for improved signal-to-noise ratiowith wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment havingmultiplexer continuous monitoring alarming features with wearable deviceintegration is disclosed.

In embodiments, system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having the useof distributed CPLD chips with dedicated bus for logic control ofmultiple MUX and data acquisition sections with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment havinghigh-amperage input capability using solid state relays and designtopology with wearable device integration is disclosed.

In embodiments, system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment havingpower-down ability of at least one of an analog sensor channel and acomponent board with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having uniqueelectrostatic protection for trigger and vibration inputs with wearabledevice integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having precisevoltage reference for A/D zero reference with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having aphase-lock loop band-pass tracking filter for obtaining slow-speed RPMsand phase information with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having digitalderivation of phase relative to input and trigger channels usingon-board timers with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having apeak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having routingof a trigger channel that is either raw or buffered into other analogchannels with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having the useof higher input oversampling for delta-sigma A/D for lower sampling rateoutputs to minimize AA filter requirements with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having the useof a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having longblocks of data at a high-sampling rate as opposed to multiple sets ofdata taken at different sampling rates with wearable device integrationis disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having storageof calibration data with maintenance history on-board card set withwearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having a rapidroute creation capability using hierarchical templates with wearabledevice integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment havingintelligent management of data collection bands with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having a neuralnet expert system using intelligent management of data collection bandswith wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having use of adatabase hierarchy in sensor data analysis with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having an expertsystem GUI graphical approach to defining intelligent data collectionbands and diagnoses for the expert system with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having agraphical approach for back-calculation definition with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having proposedbearing analysis methods with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having torsionalvibration detection/analysis utilizing transitory signal analysis withwearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having improvedintegration using both analog and digital methods with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having adaptivescheduling techniques for continuous monitoring of analog data in alocal environment with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having dataacquisition parking features with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having aself-sufficient data acquisition box with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having SD cardstorage with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having extendedonboard statistical capabilities for continuous monitoring with wearabledevice integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having the useof ambient, local and vibration noise for prediction with wearabledevice integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having smartroute changes route based on incoming data or alarms enable simultaneousdynamic data for analysis or correlation with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having smart ODSand transfer functions with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment havinghierarchical multiplexer with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment havingidentification sensory overload with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having RFidentification and an inclinometer with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment havingcontinuous ultrasonic monitoring with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment havingcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment havingcloud-based, machine pattern analysis of state information from multipleanalog industrial sensors to provide anticipated state information foran industrial system with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment havingcloud-based policy automation engine for IoT, with creation, deploymentand management of IoT devices with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having on-devicesensor fusion and data storage for industrial IoT devices with wearabledevice integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having aself-organizing data marketplace for industrial IoT data with wearabledevice integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment havingself-organization of data pools based on utilization and/or yieldmetrics with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having trainingAI models based on industry-specific feedback with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having aself-organized swarm of industrial data collectors with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having an IoTdistributed ledger with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having aself-organizing collector with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having anetwork-sensitive collector with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having aremotely organized collector with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having aself-organizing storage for a multi-sensor data collector with wearabledevice integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having aself-organizing network coding for multi-sensor data network withwearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having awearable haptic user interface for an industrial sensor data collector,with vibration, heat, electrical and/or sound outputs with wearabledevice integration is disclosed.

In integrations, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having heat mapsdisplaying collection data for AR/VR with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment havingautomatically tuned AR/VR visualization of data collected by a datacollector with wearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having processing,communications, and other IT components for remote monitoring andcontrol with wearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having a hydrogenfuel generating electrolyzer that operates on a water source to separatehydrogen and oxygen components with wearable device integration isdisclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having alow-pressure hydrogen storage system that stores the hydrogen generatedby an electrolyzer with wearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having a fuelcontrol module that automatically controls fuel sourcing or mixingdevices based on some measure of historical, current, planned, and/oranticipated consumption or availability with wearable device integrationis disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having asolar-powered hydrogen electrolyzer with wearable device integration isdisclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having awind-powered hydrogen electrolyzer with wearable device integration isdisclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having ahydro-powered hydrogen electrolyzer with wearable device integration isdisclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having an on-demandgas-LPG hybrid burner that sources LPG, hydrogen, or other fueldynamically without need for user input or monitoring with wearabledevice integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having an enclosedburner chamber that provides heat in a target heat-zone as a plane ofheat with wearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having a smart knobwith connectivity and local and remote control for controlling theintelligent cooktop device or other IoT devices with wearable deviceintegration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having a mobiledocking facility with power for charging a mobile device, datacommunications, and heat protection with wearable device integration isdisclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having distributedmodules or components that are located in sub-systems of the cooktopwith wearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having acentralized control facility to manage operation of sub-systems of thecooktop with wearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having remotecontrol capability with wearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having automationwith wearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having detectorsand sensors for monitoring cooking system conditions with wearabledevice integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having machinelearning for optimizing cooking system operation with wearable deviceintegration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having a mobileapplication with wearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having acloud-based platform that interacts with electronic devices andparticipants in a related ecosystem of suppliers, content providers,service providers, and regulators to deliver value-added services tousers of the intelligent cooking system, users of the hydrogenproduction system, and other participants of the ecosystem with wearabledevice integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having arecommendation engine for providing recommendations to users withwearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having anotification engine for providing notifications to users with wearabledevice integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having anadvertising engine for providing location-based offers to users withwearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having interfacesthat allow machine-to-machine or user-to-machine communication withother devices and the cloud, for contributing data for analytics,monitoring, control, and operation of other devices and systems withwearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having a userinterface that facilitates contextual and intelligence-drivenpersonalized experience for computing devices that connect to a networkbased around the intelligent cooking system with wearable deviceintegration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having analyticsfor profiling, recording or analyzing users, usage of the device,maintenance and repair histories, patterns relating to patterns orfaults, energy use patterns, cooking patterns, and deployment, use, andservice of electrolyzer with wearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having a commerceutility for ordering ingredients, components, and materials withwearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having a cookingassistance utility for assisting users with cooking tasks with wearabledevice integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having a healthutility for providing health indices for foods, nutritional information,nutritional search capabilities, nutritional assistance, andpersonalized suggestions and recommendations with wearable deviceintegration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having aninfotainment utility for playing music, videos, and/or podcasts withwearable device integration is disclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having abroadcasting utility for enabling a personalized cooking channel that isbroadcast from the cooking system with wearable device integration isdisclosed.

In embodiments, an intelligent cooking system using a mobile robotand/or mobile vehicle for mobile data collection and having a foodinvestigation utility for gathering information from smart cooktops anduser activity about recipes being used by users of the smart cooktopsystems throughout a region with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having an IoTplatform with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having an IoTdata adapter for receiving data inputs and establishing a connectionwith one or more available IoT cloud platforms to publish the data withwearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having acondition detector for detecting conditions related to connect attemptsmade by the IoT data adapter to one or more IoT cloud platforms withwearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having an IoTdata adapter with an adaptation engine with wearable device integrationis disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having the useof machine learning to prepare a data packet or stream with wearabledevice integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having a datamarketplace that provides a pool of available cloud networking platformswith wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data collection in an industrial environment having amessaging utility that provides a cloud platform user interface with amessage indicating the availability of a new data source and data sourceintegration and usage instructions with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability tomaintain a first and second transmission limit based on received rate ofarrival and success of delivery feedback messages, and limitingtransmission of messages based on the transmission limits with wearabledevice integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability to limittransmission of further messages not yet acknowledged as successfullydelivered according to the window size with wearable device integrationis disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability tomaintain an estimate of a rate of loss events and use it to adjust therate of redundancy messages with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having an estimated rate ofloss events where the error correction code used to determine redundancymessages chosen is based on the estimated rate of loss events withwearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability to applyforward error correction based on messages received describing channelcharacteristics with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability tomaintain/set timers based on occurrences of delivery order events anddelaying transmission of feedback messages using timers with wearabledevice integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability tomaintain/set timers based on occurrences of delivery order events anddelaying modification of congestion window size based on timers withwearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability tomaintain/set timers based on occurrences of delivery order events,delaying modification of congestion window size based on timers, andcancelling modification of congestion window size when receiving afeedback message indicating successful delivery with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability toconfigure a new connection using maintained data characterizing acurrent/previous connection with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability toconfigure a new connection using maintained data characterizing an errorrate of a current/previous connection with wearable device integrationis disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability toconfigure a new connection using maintained data characterizing a timingvariability of a current/previous connection with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability toconfigure a new connection using maintained data characterizingbandwidth of a current/previous connection with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability toconfigure a new connection using maintained data characterizing roundtrip time of a current/previous connection with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability toconfigure a new connection using maintained data characterizingcommunication control parameters of a current/previous connection withwearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability toconfigure new connection using maintained data characterizing forwarderror correction parameters of a current/previous connection withwearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having a data store formaintaining data characterizing one or more current or previous datacommunication connections and a connection initiation module forinitiating new data communication connections based on maintained datawith wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability totransmit a first subset of data messages over a lower latency data pathand a second subset of messages over a higher latency data path withwearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability totransmit a first subset of data messages that are time critical over alower latency data path and a second subset of messages over a higherlatency data path with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability totransmit a first initial subset of data messages over a lower latencydata path and a second subset of messages that are subsequentlyavailable over a higher latency data path with wearable deviceintegration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability totransmit a first subset of acknowledgment messages over a lower latencydata path and a second subset of data messages over a higher latencydata path with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability totransmit a first subset of supplemental/redundancy data messages over alower latency data path and a second subset of data messages over ahigher latency data path with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having a degree ofredundancy associated with each message that is based on messageposition in the transmission order with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having a degree ofredundancy associated with each message that increases as the positionof the messages is non-decreasing with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having a degree ofredundancy associated with each message that is based on messageposition in the transmission order and in response to receiving feedbackmessages with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having a degree ofredundancy associated with each message that is based on messageposition in the transmission order and in response to receiving feedbackmessages, and adding or removing redundancy messages from the queuebased on the feedback messages with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability to adjustthe number of messages sent over each of multiple different data pathswith different communication protocols if it is determined that a datapath is altering flow of messages with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability to adjustthe number of messages sent over each of multiple different data pathswith different communication protocols if it is determined that a datapath is altering flow of messages initial division based on previouscommunication connections with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability tomodify/add/remove redundancy information associated with encoded data asit travels from node to node via channels based on channelcharacteristics with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having the ability to sendFEC packets at an estimated rate of loss events (isolated packet loss orburst of consecutive packets) with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having combined coding, TCP,and pacing of packet transmissions with wearable device integration isdisclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having a forward errorcorrection code construction that interleaves groups of message packetsand parity packets and has coding across overlapping groups of messagepackets with wearable device integration is disclosed.

In embodiments, a system for using a mobile robot and/or mobile vehiclefor mobile data communication between nodes having a variant of TCP thatcombines delay-based back-off with a stable window increase functionwith wearable device integration is disclosed.

Systems and methods for using handheld devices for mobile datacollection within an environment for industrial IoT data collection arenext described with respect to FIGS. 293 to 296. Referring first to FIG.293, a data collection system may include one or more handheld devicesconfigured to act as mobile data collectors within an environment forindustrial IoT data collection. For example, the one or more handhelddevices may transmit data to, receive data from, transmit commands to,receive commands from, be under the control of, communicate controlsfor, or otherwise communicate with the industrial IoT data collection,monitoring and control system 10. Methods and systems are disclosedherein for data collection using handheld devices, including a singlehandheld device having a single sensor for recording state-relatedmeasurements within the environment for industrial IoT data collection,a single handheld device having multiple sensors for recordingstate-related measurements within the environment for industrial IoTdata collection, multiple handheld devices each having a single sensorfor recording state-related measurements within the environment forindustrial IoT data collection, and multiple handheld devices eachhaving one or more sensors for recording state-related measurementswithin the environment for industrial IoT data collection. For example,a handheld device may be a wearable haptic or multi-sensor userinterface for an industrial sensor data collector, with vibration, heat,electrical, and/or sound outputs. In another example, a handheld devicemay be any other suitable device, component, unit, or othercomputational aspect having a tangible form and which is configured orotherwise able to be used by disposing on a person within an industrialenvironment, regardless of the period of time of such use. Examples ofhandheld devices include, without limitation, mobile phones, laptopcomputers, tablet computers, personal digital assistants,walkie-talkies, radios, long or short range communication devices,flashlights, or any other suitable handheld devices with sensorsintegrated therein or coupled thereto. Regardless of the particularform, a handheld device according to this disclosure includes one ormore sensors for recording state-related measurements of an environmentfor industrial IoT data collection. For example, the one or more sensorsof a handheld device described in this disclosure can measure stateswith respect to equipment within an industrial IoT environment or withrespect to the industrial IoT environment itself.

A number of handheld devices 14072 are located within the environmentfor industrial IoT data collection. The handheld devices 14072 may behandheld devices issued by an operator of the environment for industrialIoT data collection. Alternatively, the handheld devices 14072 may behandheld devices owned by workers selected to perform tasks within theenvironment for industrial IoT data collection. As shown in FIG. 293,the handheld devices 14072 include a single handheld device with asingle sensor 14074, a single handheld device with multiple sensors14076, a combination of handheld devices each with a single sensor14078, and a combination of handheld devices each with one or moresensors 14080. However, in embodiments, the handheld devices 14072 mayinclude different handheld devices. For example, in embodiments, thehandheld devices 14072 may omit the combination of handheld devices eachwith the single sensor 14078 and/or the combination of handheld deviceseach with one or more of the sensors 14080. For example, the handhelddevices 14072 may be limited to individual handheld devices rather thancombinations of handheld devices that offer combined, improved orotherwise different functionality compared to each of the constituenthandheld devices taken individually. In another example, in embodiments,the handheld devices 14072 may omit the single handheld device with thesingle sensor 14074 and/or the single handheld device with multiplesensors 14076. For example, the handheld devices 14072 may be limited tocombinations of handheld devices rather than individual devices (e.g.,where specific combinations of the handheld devices are identified asbeing valuable in particular contexts or otherwise for recordingparticular state-related measurements within the environment forindustrial IoT data collection).

In embodiments, different handheld devices 14072 may be configured torecord certain types of state-related measurements of some or all of thetargets (e.g., devices or equipment) within the environment forindustrial IoT data collection. For example, some of the handhelddevices 14072 may be configured to record state-related measurementsbased on vibrations measured with respect to some or all of the targets.In another example, some of the handheld devices 14072 may be configuredto record state-related measurements based on temperatures measured withrespect to some or all of the targets. In another example, some of thehandheld devices 14072 may be configured to record state-relatedmeasurements based on electrical or magnetic outputs measured withrespect to some or all of the targets. In another example, some of thehandheld devices 14072 may be configured to record state-relatedmeasurements based on sound outputs measured with respect to some or allof the targets. In another example, some of the handheld devices 14072may be configured to record state-related measurements based on outputsother than vibrations, temperatures, electrical or magnetic, or sound,as measured with respect to some or all of the targets.

Alternatively, or additionally, different handheld devices 14072 may beconfigured to record some or all state-related measurements of certaintypes of the targets within the environment for industrial IoT datacollection. For example, some of the handheld devices 14072 may beconfigured to record some or all state-related measurements fromagitators (e.g., turbine agitators), airframe control surface vibrationdevices, catalytic reactors, compressors, and the like. In anotherexample, some of the handheld devices 14072 may be configured to recordsome or all state-related measurements from conveyors and lifters,disposal systems, drive trains, fans, irrigation systems, motors, andthe like. In another example, some of the handheld devices 14072 may beconfigured to record some or all state-related measurements frompipelines, electric powertrains, production platforms, pumps (e.g.,water pumps), robotic assembly systems, thermic heating systems, tracks,transmission systems, turbines, and the like. In embodiments, thehandheld devices 14072 may be configured to record some or allstate-related measurements of certain types of industrial environments.For example, an industrial environment having targets with statesmeasured using the handheld devices 14072 may include, but is notlimited to, a manufacturing environment, a fossil fuel energy productionenvironment, an aerospace environment, a mining environment, aconstruction environment, a ship environment, a shipping environment, asubmarine environment, a wind energy production environment, ahydroelectric energy production environment, a nuclear energy productionenvironment, an oil drilling environment, an oil pipeline environment,any other suitable energy product environment, any other suitable energyrouting or transmission environment, any other suitable industrialenvironment, a factory, an airplane or other aircraft, a distributionenvironment, an energy source extraction environment, an offshoreexploration site, an underwater exploration site, an assembly line, awarehouse, a power generation environment, a hazardous wasteenvironment, and the like.

In embodiments, the state-related measurements using the handhelddevices 14072 may be made available over the network 14010 (e.g., asdescribed with respect to FIG. 286) without the need for externalnetworks. The network 14010 may be a MANET (e.g., the MANET 20 shown inFIG. 2 or any other suitable MANET n), the Internet (e.g., the Internet110 shown in FIG. 3 or any other suitable Internet), or any othersuitable type of network, or any combination thereof. For example, thenetwork 14010 may be used to receive state-related measurements recordedusing the handheld devices 14072. The network 14010 may then be used totransmit some or all of those received state-related measurements toother components of the data collection system 102. For example, thenetwork 14010 may be used to transmit some or all of the receivedstate-related measurements to data pool 14084 (e.g., the data pool 60shown in FIG. 2 or any other suitable data pool) for storage of thosereceived state-related measurements. In another example, the network14010 may be used to transmit some or all of the received state-relatedmeasurements to servers 14086 of the environment for industrial IoT datacollection (e.g., the servers 14014 shown in FIG. 286, or any othersuitable server). The servers 14086 may include one or more hardware orsoftware server aspects. For example, the servers 14086 to which thereceived state-related measurements are transmitted may includeintelligent systems 14088 for processing the received state-relatedmeasurements. The intelligent systems 14088 may process the receivedstate-related measurements using artificial intelligence processes,machine learning processes, and/or other cognitive processes to identifyinformation within or otherwise associated with the receivedstate-related measurements. In embodiments, after processing thereceived state-related measurements, the servers 14086 to which thereceived state-related measurements are transmitted may transmit theprocessed information or data indicative of the processed information toother systems (e.g., for storage or analysis). The data indicative ofthe processed information from the servers 14086 may include, forexample, output or other results of the artificial intelligenceprocesses, machine learning processes, and/or other cognitive processes.

In embodiments, some or all of the handheld devices 14072 may includeintelligent systems 14082 for processing the state-related measurementsrecorded using those handheld devices 14072 before transmitting thoserecorded state-related measurements (e.g., over the network 14010 or anyother suitable communication mechanism). For example, some or all of thehandheld devices 14072 may integrate artificial intelligence processes,machine learning processes, and/or other cognitive processes foranalyzing the state-related measurements recorded thereby. Theprocessing by the intelligent systems 14082 of the handheld devices14072 may be or be represented within a pre-processing step of theindustrial IoT data collection, monitoring and control system 10. Forexample, the pre-processing may be selectively performed by certaintypes of the handheld devices 14072 to pre-process the recordedstate-related measurements (e.g., to identify redundant information,irrelevant information, or insignificant information). In anotherexample, the pre-processing may be automated for certain types of thehandheld devices 14072 to pre-process the recorded state-relatedmeasurements (e.g., to identify redundant information, irrelevantinformation, or insignificant information). In another example, thepre-processing may be selectively performed for certain types ofstate-related measurements recorded by any of the handheld devices 14072to pre-process the recorded state-related measurements (e.g., toidentify redundant information, irrelevant information, or insignificantinformation). In another example, the pre-processing may be automatedfor certain types of state-related measurements recorded by any of thehandheld devices 14072 to pre-process the recorded state-relatedmeasurements (e.g., to identify redundant information, irrelevantinformation, or insignificant information).

In embodiments, some or all of the handheld devices 14072 may includesensor fusion functionality. For example, the sensor fusionfunctionality may be embodied as the on-device sensor fusion 80. Forexample, state-related measurements recorded using multiple analogsensors of one or more of the handheld devices 14072 (e.g., the multipleanalog sensors 82 shown in FIG. 4 or any other suitable sensor) may belocally or remotely processed using artificial intelligence processes,machine learning processes, and/or other cognitive processes, which maybe embodied within the handheld devices 14072 themselves, the servers14086, or both. The sensor fusion functionality may be embodied by apre-processing step that is performed prior to the artificialintelligence processes, machine learning processes, and/or othercognitive processes. In embodiments, the sensor fusion functionality maybe performed using a MUX. For example, each of the single handhelddevices with multiple sensors 14076 may include its own MUX forcombining state-related measurements recorded using different individualsensors of those multiple sensors. In another example, some or all ofthe individual handheld devices within the combination of handhelddevices each with one or more sensors 14080 may include its own MUX forcombining state-related measurements recorded using different individualsensors of those multiple sensors. In some such embodiments, the MUX maybe internal to those handheld devices. In some such embodiments, the MUXmay be external to those handheld devices.

The handheld devices 14072 may be controlled by or otherwise used inconnection within the host processing system 112 shown in FIG. 6 (or anyother suitable host system). The host processing system 112 may belocally accessible over the network 14010. Alternatively, the hostprocessing system 112 may be remote (e.g., as embodied in a cloudcomputing system), may be accessible using one or more networkinfrastructure elements (e.g., access points, switches, routers,servers, gateways, bridges, connectors, physical interfaces and thelike), and/or use one or more network protocols (e.g., IP-basedprotocols, TCP/IP, UDP, HTTP, Bluetooth, Bluetooth Low Energy, cellularprotocols, LTE, 2G, 3G, 4G, 5G, CDMA, TDSM, packet-based protocols,streaming protocols, file transfer protocols, broadcast protocols,multi-cast protocols, unicast protocols, and the like). In embodiments,the state-related measurements recorded using the handheld devices 14072may be processed using a network coding system or method, which may beembodied internally or externally with respect to the host processingsystem 112. For example, the network coding system can process themeasurements recorded using the handheld devices 14072 based on theavailability of networks for communicating those recorded state-relatedmeasurements, based on the availability of bandwidth and spectrum forcommunicating those recorded state-related measurements, based on othernetwork characteristics, or based on some combination thereof.

In embodiments, the state-related measurements recorded using thehandheld devices 14072 may be pulled from the handheld devices 14072 byan upstream device (e.g., a client device or other software or hardwareaspect used to review, analyze, or otherwise view the state-relatedmeasurements). For example, the handheld devices 14072 may not activelytransmit the state-related measurements that are received (e.g., at theservers 14086, the data pool 14084, or any other suitable hardware orsoftware component that receives the state-related measurements recordedusing the handheld devices 14072). Rather, the transmission of thestate-related measurements from the handheld devices 14072 may be causedby commands received at the handheld devices 14072 (e.g., from servers14086 or from other hardware or software of the data collection system102). For example, a data collector, which may be fixed within aparticular location of the environment of industrial IoT data collectionor mobile therein, may be configured to pull state-related measurementsrecorded using various handheld devices 14072. For example, the handhelddevices 14072 may continuously, periodically, or otherwise at multipletimes record state-related measurements within the environment forindustrial IoT data collection. The data collector may, at fixedintervals, at random times, or otherwise, transmit one or more commandsto some or all of the handheld devices 14072 to pull some or all of thestate-related measurements recorded using those handheld devices 14072since the last time state-related measurements were pulled therefrom.Alternatively, the data collector may, at those fixed intervals, atthose random times, or otherwise, transmit the one or more commands to acollective processing mind 14090 associated with the handheld devices14072. For example, the collective processing mind 14090 may be orinclude a hub for receiving the state-related measurements recordedusing some or all of the handheld devices 14072. In another example, thecommands, when processed using individual handheld devices 14072 or bythe collective processing mind 14090 of the handheld devices 14072,cause the recorded state-related measurements or data representativethereof to be transmitted from the handheld devices 14072. For example,the collective processing mind 14090 may be configured to pull thestate-related measurements from some or all of the handheld devices14072 (e.g., at fixed intervals, at random times, or otherwise). Thecollective processing mind 14090 may then transmit the state-relatedmeasurements pulled from the handheld devices 14072 (e.g., to theservers 14086, the data pool 14084, or the other hardware or softwarecomponent selected or otherwise configured to receive the state-relatedmeasurements).

In embodiments, the state-related measurements recorded using thehandheld devices 14072 may be transmitted from the handheld devices14072 responsive to requests for those state-related measurements. Forexample, the collective processing mind 14090 may, at fixed intervals,at random times, or otherwise, transmit a request for recordedstate-related measurements to some or all of the handheld devices 14072.The processors of the some or all of the handheld devices 14072 to whichthe request is sent may process the request to determine whichstate-related measurements to transmit. For example, data indicative ofa time of a most recent request for recorded state-related measurementsmay be accessed by those processors. The processors may then comparethat time to a time at which the new request is received from thecollective processing mind 14090. The processors may then query a datastore for state-related measurements recorded between the two times. Theprocessors may then transmit those state-related measurements inresponse to the request. In another example, the processors may identifya most recent set of state-related measurements recorded using thecorresponding handheld devices 14072 and transmit those state-relatedmeasurements in response to the request. In another example, datacollectors within the data collection system 10 may transmit the requestdirectly to the handheld devices 14072. In yet another example, the datacollectors may transmit the request to the collective processing mind14090. The collective processing mind 14090 may process the request todetermine select individual handheld devices 14072 which were used torecord the requested state-related measurements. The collectiveprocessing mind 14090 may then transmit certain state-relatedmeasurements in response to the request by, for example, querying astorage for some or all of the state-related measurements recorded usingthose select individual handheld devices 14072. Alternatively, thecollective processing mind 14090 may process the request to determinewhich of the state-related measurements recorded by some or all of thehandheld devices 14072 to transmit in response to the request (e.g.,based on a time of the request). For example, the collective processingmind 14090 can compare the time of the request to a time of a mostrecent request for recorded state-related measurements. The collectiveprocessing mind 14090 can then retrieve the state-related measurementsrecorded in between those times and transmit the retrieved state-relatedmeasurements in response to the request.

In embodiments, the state-related measurements recorded using thehandheld devices 14072 may be pushed from the handheld devices 14072 toan upstream device (e.g., a client device or other software or hardwareaspect used to review, analyze, or otherwise view the state-relatedmeasurements). For example, the handheld devices 14072 may activelytransmit the state-related measurements that are received (e.g., at theservers 14086, the data pool 14084, or any other suitable hardware orsoftware component that receives the state-related measurements recordedusing the handheld devices 14072), without such receiving hardware orsoftware component requesting those state-related measurements orotherwise causing the handheld device to transmit those state-relatedmeasurements based on a command. For example, some or all of thehandheld devices 14072 may transmit state-related measurements on afixed interval, at random times, immediately upon the recording of thosestate-related measurements, some amount of time after recording thosemeasurements, upon a determination that a threshold number ofstate-related measurements have been recorded, or at other suitabletimes. In some such embodiments, the handheld devices 14072, either bythemselves or using the collective processing mind 14090, may push therecorded state-related measurements in response to detecting a nearproximity of a data collection router 14092.

For example, referring next to FIG. 294, the collective processing mind14090 may include a detector 14094 configured to detect a near proximityof a target 14096 (e.g., one of the devices 13006 shown in FIG. 180 orany other suitable target) with respect to one or more of the handhelddevices 14072. For example, upon such a detection, the collectiveprocessing mind 14090 may send a signal to the one or more of thehandheld devices 14072 to record and transmit state-related measurementsof receipt at the data collection router 14092. Alternatively, upon sucha detection, the collective processing mind 14090 may query a data storeto retrieve state-related measurements and then transmit thosestate-related measurements of receipt at the data collection router14092. In either case, the data collection router 14092 forwards thereceived state-related measurements to the servers 14086, the data pool14084, or any other suitable hardware or software component. In anotherexample, upon such a detection, the collective processing mind 14090 maysend the signal directly to the servers 14086, the data pool 14084, orthe other hardware or software component, for example, to bypass thedata collection router 14092 or where the data collection router 14092is omitted.

Referring next to FIG. 295, in embodiments, the collective processingmind 14090 may be omitted. Instead, the handheld devices 14072 detectthe near proximity of the target 14096. Upon such detection using thehandheld devices 14072 (e.g., one or more of the single handheld devicewith the single sensor 14074, the single handheld device with multiplesensors 14076, the combination of handheld devices each with the singlesensor 14078, or the combination of handheld devices each with one ormore sensors 14080), the handheld devices 14072 record state-relatedmeasurements of the target 14096 (e.g., vibrations, temperature,electrical or magnetic output, sound output, or the like). The recordedstate-related measurements can be transmitted over the network 14010(e.g., to the data pool 14084, the servers 14086, or any other suitablehardware or software component). Alternatively, the recordedstate-related measurements can be transmitted to the data collectionrouter 14092, for example, where the network 14010 is unavailable orwhere the data collection router 14092 is configured to receive and/orpre-process the recorded state-related measurements from the handhelddevices 14072. The data collection router 14092 may be one of a numberof data collection routers 14092 located throughout the environment forindustrial IoT data collection. For example, the data collection router14092 may be a data collection router 14092 configured to transmitstate-related measurements specifically recorded for the target 14096.

Referring next to FIG. 296, various aspects of functionality ofintelligent systems 14098 used to process output of the handheld devices14072 are disclosed. The intelligent systems 14098 include a cognitivelearning module 14100, an artificial intelligence module 14102, and amachine learning module 14104. In embodiments, the intelligent systems14098 may include additional or fewer modules. The intelligent systems14098 may, for example, be the intelligent systems 14082 or theintelligent systems 14088 shown in FIG. 286 or any other suitableintelligent system. Although shown as separate modules, in embodiments,there may be overlap between some or all of the cognitive learningmodule 14100, the artificial intelligence module 14102, and the machinelearning module 14104. For example, the artificial intelligence module14102 may include the machine learning module 14104. In another example,the cognitive learning module 14100 may include the artificialintelligence module 14102 (and, in embodiments, therefore, the machinelearning module 14104). The handheld devices 14072 may include anynumber of handheld devices. For example, as shown, the handheld devices14072 include a first handheld device 14072A, a second handheld device14072B, and an Nth handheld device 14072N, where N is a number greaterthan two. The intelligent systems 14098 receives the output of thehandheld devices 14072A, 14072B, . . . 14072N. In particular, one ormore of the modules 14100, 14102, and 14104 of the intelligent systems14098 receives data generated by and output from one or more of thehandheld devices 14072A, 14072B, . . . 14072N. The output from thehandheld devices 14072A, 14072B, . . . 14072N may, for example, includestate-related measurements recorded using the handheld devices 14072A,14072B, . . . 14072N, for example, state-related measurements ofequipment within an environment for industrial IoT data collection. Inembodiments, the output from the handheld devices 14072A, 14072B, . . .14072N may be processed by all three of the modules 14100, 14102, and14104 of the intelligent systems 14098. In embodiments, the output fromthe handheld devices 14072A, 14072B, . . . 14072N may be processed byonly one of the modules 14100, 14102, and 14104 of the intelligentsystems 14098. For example, the particular one of the modules 14100,14102, and 14104 of the intelligent systems 14098 to use to process theoutput from the handheld devices 14072A, 14072B, . . . 14072N may beselected based on the handheld device used to generate that output, theequipment measured in generating that output, the values of the output,other selection criteria, and the like.

The knowledge base 14036 (e.g., as shown in FIG. 289) may be updatedbased on output from the intelligent systems 14098. The knowledge base14036 represents a library or other set or collection of knowledgerelated to the environment of the industrial IoT data collection,including equipment within that environment, tasks performed within thatenvironment, personnel having the skill to perform tasks within thatenvironment, and the like. The intelligent systems 14098 can process thestate-related measurements recorded using the handheld devices 14072A,14072B, . . . 14072N to facilitate knowledge gathering for expanding theknowledge base 14036. For example, the modules 14100, 14102, and 14104of the intelligent systems 14098 can process those state-relatedmeasurements against existing knowledge within the knowledge base 14036to update or otherwise modify information within the knowledge base14036. The intelligent systems 14098 may use intelligence and machinelearning capabilities (e.g., of the machine learning module 14104 or asdescribed elsewhere in this disclosure) to process state-relatedmeasurements and related information based on detected conditions (e.g.,conditions informed by the handheld devices 14072 and/or provided astraining data) and/or state information (e.g., state informationdetermined by a machine state recognition system that may determine astate, for example, relating to an operational state, an environmentalstate, a state within a known process or workflow, a state involving afault or diagnostic condition, and the like). This may includeoptimization of input selection and configuration based on learningfeedback from the learning feedback system, which may include providingtraining data (e.g., from a host processing system or from other datacollection systems either directly or from the host processing system)and may include providing feedback metrics (e.g., success metricscalculated within an analytic system of the host processing system).Examples of host processing systems, learning feedback systems, datacollection systems, and analytic systems are described elsewhere in thisdisclosure. Thus, the intelligent systems 14098 can be used to updateworkflows of tasks assigned and performed within the industrial IoTenvironment based on output from the handheld devices 14072A, 14072B, .. . 14072N.

In embodiments, the intelligent systems 14098, either within one of themodules 14100, 14102, and 14104 or otherwise, may include otherintelligence or machine learning aspects. For example, the intelligentsystems 14098 may include one or more of a YOLO neural network, a YOLOCNN, a set of neural networks configured to operate on or from a FPGA, aset of neural networks configured to operate on or from a FPGA and GPUhybrid component, a user configurable series and parallel flow for ahybrid neural network (e.g., configuring series and/or parallel flowsbetween neural networks as outputs which can be communicated betweensuch neural networks), a machine learning system for automaticallyconfiguring a topology or workflow for a set of hybrid neural networks(e.g., series, parallel, data flows, etc.) based on a training data setwhich may or may not use manual configurations (e.g., by a human user),a deep learning system for automatically configuring a topology orworkflow for a set of hybrid neural networks (e.g., series, parallel,data flows, etc.) based on a training data set of outcomes fromindustrial IoT processes (e.g., maintenance, repair, service, predictionof faults, optimization of operation of a machine, system of facility,etc.), or other intelligence or machine learning aspects.

Thus, in embodiments, the output of the handheld devices 14072 may beprocessed using the intelligent systems 14088 to add to, remove from, orotherwise modify the knowledge base 14036. For example, the knowledgebase 14036 may reflect information to use to perform one or more taskswithin the industrial environment in which the targets are located andin which the handheld devices 14072 are used. The output from thehandheld devices 14072 can thus be used to increase knowledge as to thenature of issues that arise with respect to the industrial environment,for example, by describing information about the target from whichmeasurements were recorded, a time and/or date at which the measurementswere recorded, pre-existing state or other condition information aboutthe target, information about the time required to resolve an issue withrespect to a target, information about how to resolve an issue withrespect to a target, information indicating an amount of downtime to thetarget and to other aspects of the respective industrial environmentresulting from resolving the issue, an indication of whether the issueshould be resolved now or later (or not at all), and the like. Theintelligent systems 14088 may process that output to update existingtraining data. For example, the existing training data can be used toupdate the machine learning, artificial intelligence, and/or othercognitive functionality for identifying states of targets based on theoutput of the handheld devices 14072.

For example, the knowledge base 14036 may include a series of databasesor other tables or graphs arranged hierarchically based on the target orthe area of the industrial environment that includes the target. Forexample, a first layer of the knowledge base 14036 may refer to theindustrial environment (e.g., a power plant, a manufacturing facility, amining facility, etc.). A second layer of the knowledge base 14036 mayrefer to zones within the industrial environment (e.g., zone 1, zone 2,etc., or named zones, as the case may be). A third layer of theknowledge base 14036 may refer to targets within those zones (e.g.,within a first zone of a power plant including electrical equipment,this could include alternators, circuit breakers, transformers,batteries, exciters, etc., and, within a second zone of a power plantincluding a turbine, a generator, a generator magnet, etc.). Theknowledge base 14036 may be updated based on output of the intelligentsystems 14088, by manual user data entry, or both. For example, a workerwithin manufacturing facility may be given one or more handheld devices(e.g., the handheld devices 14072). The worker may walk around themanufacturing facility and approach several pieces of machinery indifferent zones, including a hydraulic press within a first zone, athermoforming machine within a second zone, and a conveyor within athird zone. In approaching the first zone, the handheld device mayrecord a measurement with respect to the hydraulic press indicating avibration resulting from the operation of the hydraulic press. Thatmeasurement is then processed using the intelligent systems 14088, forexample, against data stored in a database for the hydraulic presswithin the knowledge base 14036. In the event the measurement isinconsistent with the data stored in that database, the intelligentsystem 14088 may determine that the hydraulic press is not operatingproperly. For example, if the vibration resulting from the operation ofthe hydraulic press is less than what is recorded in the knowledge base14036, it may be determined that the hydraulic press is not functioningat an optimal rate. The data within the knowledge base 14036 may then beconsulted to determine the likely causes of this issue, including howmuch time would be required to resolve it. For example, the knowledgebase 14036 can indicate that low vibration output is caused by aparticular part failure with respect to the hydraulic press.

The worker may then walk to the thermoforming machine and use thehandheld device to measure an ambient temperature around that machine.The measurement is processed using the intelligent systems 14088 todetermine that the thermoforming machine is outputting an expectedtemperature. The worker may then walk to the conveyor and use thehandheld machine to measure the velocity of the conveyor. For example, acamera vision system built into the handheld device may be used todetect an operating velocity of the conveyor. The operating velocity maythen be compared against the expected operating velocity for theconveyor as shown in the appropriate section of the knowledge base14036. Upon a determination that the conveyor is operating at anunexpected velocity, the intelligent systems 14088, such as through thehandheld device or through a collective processing mind in communicationwith the handheld device (e.g., the collective processing mind locatedwithin the third zone of the manufacturing facility) may alert workersin the area of the conveyor that the conveyor may not be functioning asintended. The alert may be represented as a warning notification so asto prevent sudden emergency action from being taken. In such a scenario,a worker may see the alert and update the knowledge base 14036 toreflect the unexpected velocity measurement.

Disclosed herein are systems for using handheld devices for datacollection in an industrial environment. As used herein, handheld deviceintegration refers to using handheld devices for specific or generalpurposes. For example, handheld device integration as described withrespect to the functionality or configuration of a system refers to theuse by that system of the handheld devices 14072 and/or the hardwareand/or software used in connection with the handheld devices 14072 fordata collection within an industrial IoT environment, as shown in FIGS.293 to 296. Such use of handheld devices refers to the use of one ormore of the handheld devices 14072. For example, a system disclosedherein as using a handheld device may include using one or more of amobile phone, laptop computer, tablet computer, personal digitalassistant, walkie-talkie, radio, long or short range communicationdevice, flashlight, or other types of handheld devices.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having the use of an analog cross pointswitch for collecting variable groups of analog sensor inputs isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having IP front signal conditioning on amultiplexer for improved signal-to-noise ratio is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having multiplexer continuous monitoringalarming features is disclosed.

In embodiments, system for data collection in an industrial environmenthaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having high-amperage input capability usingsolid state relays and design topology is disclosed.

In embodiments, system for using a handheld device for data collectionin an industrial environment having power-down ability of at least oneof an analog sensor channel and a component board is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having unique electrostatic protection fortrigger and vibration inputs is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having precise voltage reference for A/Dzero reference is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a phase-lock loop band-pass trackingfilter for obtaining slow-speed RPMs and phase information is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having digital derivation of phase relativeto input and trigger channels using on-board timers is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a peak-detector for auto-scalingthat is routed into a separate analog-to-digital converter for peakdetection is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having routing of a trigger channel that iseither raw or buffered into other analog channels is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having the use of higher input oversamplingfor delta-sigma A/D for lower sampling rate outputs to minimize AAfilter requirements is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having the use of a CPLD as a clock-dividerfor a delta-sigma analog-to-digital converter to achieve lower samplingrates without the need for digital resampling is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having storage of calibration data withmaintenance history on-board card set is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a rapid route creation capabilityusing hierarchical templates is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having intelligent management of datacollection bands is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a neural net expert system usingintelligent management of data collection bands is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having use of a database hierarchy insensor data analysis is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a graphical approach forback-calculation definition is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having proposed bearing analysis methods isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having improved integration using bothanalog and digital methods is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having adaptive scheduling techniques forcontinuous monitoring of analog data in a local environment isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having data acquisition parking features isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a self-sufficient data acquisitionbox is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having SD card storage is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having extended onboard statisticalcapabilities for continuous monitoring is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having the use of ambient, local andvibration noise for prediction is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having smart route changes route based onincoming data or alarms enable simultaneous dynamic data for analysis orcorrelation is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having smart ODS and transfer functions isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having hierarchical multiplexer isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having identification sensory overload isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having RF identification and aninclinometer is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having continuous ultrasonic monitoring isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having cloud-based policy automation enginefor IoT, with creation, deployment and management of IoT devices isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having on-device sensor fusion and datastorage for industrial IoT devices is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a self-organizing data marketplacefor industrial IoT data is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having self-organization of data poolsbased on utilization and/or yield metrics is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having training AI models based onindustry-specific feedback is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a self-organized swarm of industrialdata collectors is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having an IoT distributed ledger isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a self-organizing collector isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a network-sensitive collector isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a remotely organized collector isdisclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a self-organizing storage for amulti-sensor data collector is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a self-organizing network coding formulti-sensor data network is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a wearable haptic user interface foran industrial sensor data collector, with vibration, heat, electricaland/or sound outputs is disclosed.

In integrations, a system for using a handheld device for datacollection in an industrial environment having heat maps displayingcollection data for AR/VR is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving processing, communications, and other IT components for remotemonitoring and control is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a hydrogen fuel generating electrolyzer that operates on a watersource to separate hydrogen and oxygen components is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a low-pressure hydrogen storage system that stores the hydrogengenerated by an electrolyzer is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a fuel control module that automatically controls fuel sourcingor mixing devices based on some measure of historical, current, planned,and/or anticipated consumption or availability is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a solar-powered hydrogen electrolyzer is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a wind-powered hydrogen electrolyzer is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a hydro-powered hydrogen electrolyzer is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving an on-demand gas-LPG hybrid burner that sources LPG, hydrogen, orother fuel dynamically without need for user input or monitoring isdisclosed.

In embodiments, an intelligent system for using a handheld device andhaving an enclosed burner chamber that provides heat in a targetheat-zone as a plane of heat is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a smart knob with connectivity and local and remote control forcontrolling the intelligent cooktop device or other IoT devices isdisclosed.

In embodiments, an intelligent system for using a handheld device andhaving a mobile docking facility with power for charging a mobiledevice, data communications, and heat protection is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving distributed modules or components that are located in sub-systemsof the cooktop is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a centralized control facility to manage operation of sub-systemsof the cooktop is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving remote control capability is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving automation is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving detectors and sensors for monitoring cooking system conditions isdisclosed.

In embodiments, an intelligent system for using a handheld device andhaving machine learning for optimizing cooking system operation isdisclosed.

In embodiments, an intelligent system for using a handheld device andhaving a mobile application is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a cloud-based platform that interacts with electronic devices andparticipants in a related ecosystem of suppliers, content providers,service providers, and regulators to deliver value-added services tousers of the intelligent cooking system, users of the hydrogenproduction system, and other participants of the ecosystem is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a recommendation engine for providing recommendations to users isdisclosed.

In embodiments, an intelligent system for using a handheld device andhaving a notification engine for providing notifications to users isdisclosed.

In embodiments, an intelligent system for using a handheld device andhaving an advertising engine for providing location-based offers tousers is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving interfaces that allow machine-to-machine or user-to-machinecommunication with other devices and the cloud, for contributing datafor analytics, monitoring, control, and operation of other devices andsystems is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a user interface that facilitates contextual andintelligence-driven personalized experience for computing devices thatconnect to a network based around the intelligent cooking system isdisclosed.

In embodiments, an intelligent system for using a handheld device andhaving analytics for profiling, recording or analyzing users, usage ofthe device, maintenance and repair histories, patterns relating topatterns or faults, energy use patterns, cooking patterns, anddeployment, use, and service of electrolyzer is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a commerce utility for ordering ingredients, components, andmaterials is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a cooking assistance utility for assisting users with cookingtasks is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a health utility for providing health indices for foods,nutritional information, nutritional search capabilities, nutritionalassistance, and personalized suggestions and recommendations isdisclosed.

In embodiments, an intelligent system for using a handheld device andhaving an infotainment utility for playing music, videos, and/orpodcasts is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a broadcasting utility for enabling a personalized cookingchannel that is broadcast from the cooking system is disclosed.

In embodiments, an intelligent system for using a handheld device andhaving a food investigation utility for gathering information from smartcooktops and user activity about recipes being used by users of thesmart cooktop systems throughout a region is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having an IoT platform is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having an IoT data adapter for receivingdata inputs and establishing a connection with one or more available IoTcloud platforms to publish the data is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a condition detector for detectingconditions related to connect attempts made by the IoT data adapter toone or more IoT cloud platforms is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having an IoT data adapter with anadaptation engine is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having the use of machine learning toprepare a data packet or stream is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a data marketplace that provides apool of available cloud networking platforms is disclosed.

In embodiments, a system for using a handheld device for data collectionin an industrial environment having a messaging utility that provides acloud platform user interface with a message indicating the availabilityof a new data source and data source integration and usage instructionsis disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to maintain a first andsecond transmission limit based on received rate of arrival and successof delivery feedback messages, and limiting transmission of messagesbased on the transmission limits is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to maintain an estimateof a rate of loss events and use it to adjust the rate of redundancymessages is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having an estimated rate of loss eventswhere the error correction code used to determine redundancy messageschosen is based on the estimated rate of loss events is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicsis disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to maintain/set timersbased on occurrences of delivery order events and delaying transmissionof feedback messages using timers is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to maintain/set timersbased on occurrences of delivery order events and delaying modificationof congestion window size based on timers is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to maintain/set timersbased on occurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to configure a newconnection using maintained data characterizing a current/previousconnection is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to configure a newconnection using maintained data characterizing an error rate of acurrent/previous connection is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to configure a newconnection using maintained data characterizing a timing variability ofa current/previous connection is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to configure a newconnection using maintained data characterizing bandwidth of acurrent/previous connection is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to configure a newconnection using maintained data characterizing round trip time of acurrent/previous connection is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to configure a newconnection using maintained data characterizing communication controlparameters of a current/previous connection is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to configure newconnection using maintained data characterizing forward error correctionparameters of a current/previous connection is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to transmit a firstsubset of data messages over a lower latency data path and a secondsubset of messages over a higher latency data path is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to transmit a firstsubset of data messages that are time critical over a lower latency datapath and a second subset of messages over a higher latency data path isdisclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to transmit a firstinitial subset of data messages over a lower latency data path and asecond subset of messages that are subsequently available over a higherlatency data path is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to transmit a firstsubset of acknowledgment messages over a lower latency data path and asecond subset of data messages over a higher latency data path isdisclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to transmit a firstsubset of supplemental/redundancy data messages over a lower latencydata path and a second subset of data messages over a higher latencydata path is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having a degree of redundancy associatedwith each message that is based on message position in the transmissionorder is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having a degree of redundancy associatedwith each message that increases as the position of the messages isnon-decreasing is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having a degree of redundancy associatedwith each message that is based on message position in the transmissionorder and in response to receiving feedback messages is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having a degree of redundancy associatedwith each message that is based on message position in the transmissionorder and in response to receiving feedback messages, and adding orremoving redundancy messages from the queue based on the feedbackmessages is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to modify/add/removeredundancy information associated with encoded data as it travels fromnode to node via channels based on channel characteristics is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having combined coding, TCP, and pacing ofpacket transmissions is disclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets isdisclosed.

In embodiments, a system for using a handheld device for datacommunication between nodes having a variant of TCP that combinesdelay-based back-off with a stable window increase function isdisclosed.

Systems and methods for identifying operating characteristics, such asvibration, of one or more targets, as described and which may bereferred to herein as devices, within an industrial IoT environmentusing image data sets are described with respect to FIGS. 297-299. Inembodiments, a system, such as a computer vision system 15000 generallyillustrated in FIG. 297, is configured to detect vibration or otheroperating characteristics (e.g., vibration, heat, electromagneticemissions, or other suitable operating characteristics) of the one moretargets in the industrial IoT environment (e.g., as described above)using one or more image data sets. The one or more targets may includethe devices 13006, as described above. The devices 13006 may includeagitators, including turbine agitators, airframe control surfacevibration devices, catalytic reactors and compressors. The devices 13006may also include conveyors and lifters, disposal systems, drive trains,fans, irrigation systems and motors.

The devices 13006 may also include pipelines, electric powertrains,production platforms, pumps (e.g., water pumps), robotic assemblysystems, thermic heating systems, tracks, transmission systems andturbines. The devices 13006 may operate within a single industrialenvironment 13018 or multiple industrial environments 13018. Forexample, a pipeline device may operate within an oil and gasenvironment, while a catalytic reactor may operate in either an oil andgas production environment or a pharmaceutical environment. Inembodiments, an operator, as described throughout this disclosure,operating, supervising, inspecting, or a combination thereof, one ormore of the devices 13006 may use the computer vision system 15000 toanalyze the operation of the one or more devices 13006. In embodiments,the operator may review data, reports, charts, or other suitable outputfrom the computer vision system 15000 to determine whether maintenance,repair, or other suitable interaction with the one or more devices 13006is required. For example, the output from the computer vision system15000 may indicate that vibration associated with one of the devices13006 may lead to a failure if a particular component of the device13006 is not replaced or repaired within a particular timeframe. Inembodiments, the computer vision system 15000 may be configured toanalyze image data sets, as will be described, and identify one or moreissues (e.g., faults or potential failures of one or more components),determine a corrective action (e.g., alter an operating speed of adevice associated with the faulty or failing component), and initiatethe corrective action (e.g., automatically analyze data, identifyissues, determine corrective action, and carry out, at least part of,the corrective action).

A computer vision system, such as the computer vision system 15000, maybe adapted to automate tasks and/or features of human visual systems.For example, the computer vision system 15000 may be configured tocapture image data associated with the devices 13006 and analyze theimage data using various visual techniques that simulate and improve onaspects of human sight and analysis. For example, unlike human sight,the computer vision system 15000 may enhance an image by zooming in onan object, analyzing individual frames and deltas between frames. Inanother example, the computer vision system 15000 may also captureimages outside the typical human perceptible range, such as ultra-violetor infra-red signals. The computer vision system 15000 may then identifyvarious characteristics of the devices 13006, such as the presence oramount of undesirable vibration, using the visual techniques. Thecomputer vision system 15000 may be trained, such as by a human operatoror supervisor, or based on a data set, model, or the like. Training mayinclude presenting the computer vision system 15000 with one or moretraining data sets that represent values, such as sensor data, eventdata, parameter data, and other types of data (including the many typesdescribed throughout this disclosure), as well as one or more indicatorsof an outcome, such as an outcome of a process, an outcome of acalculation, an outcome of an event, an outcome of an activity, or thelike. Training may include training in optimization, such as trainingthe computer vision system 15000 to optimize one or more systems basedon one or more optimization approaches, such as Bayesian approaches,parametric Bayes classifier approaches, k-nearest-neighbor classifierapproaches, iterative approaches, interpolation approaches, Paretooptimization approaches, algorithmic approaches, and the like. Feedbackmay be provided in a process of variation and selection, such as with agenetic algorithm that evolves one or more solutions based on feedbackthrough a series of rounds. Feedback may be determined and provided by ahuman operator or by another component of a monitoring system.

In embodiments, the computer vision system 15000 may be trained usingtraining data sets that include visual and/or non-visual data toidentify operating characteristics of the devices 13006 using the datacaptured by one or more data capture devices 15002. In embodiments, thetraining data sets may include image data corresponding to variousoperating states of components of the devices 13006. For example, thetraining data sets may include image data corresponding to components ofthe devices 13006 operating within expected or acceptable conditions ortolerances, image data corresponding to components of the devices 13006operating beyond the expected or acceptable conditions or tolerances,image data corresponding to components of the devices 13006 operatingwithin the expected or acceptable conditions or tolerances, but aretrending toward not operating within the expected or acceptableconditions or tolerances.

In embodiments, the training data sets may be generated based on imagedata of the components of the devices 13006 or similar devices and datacaptured various sensors (e.g., vibration sensors as describedthroughout this disclosure). For example, the training data sets mayinclude a correlation of image data with sensed vibrations of componentsof the devices 13006 (e.g., image data indicating a component isoperating within the expected or acceptable conditions or tolerances maybe correlated with sensed vibration data that indicates the vibration isexpected or acceptable).

In embodiments, the computer vision system 15000 may capture data fromthe devices 13006 (e.g., image data), using various visual inputdevices. For example, the data capture devices 15002 may capture data,such as visual or image data, during operation of the devices 13006. Forexample, the data captures devices 15002 may capture a plurality ofimages over a period of time (e.g., during which the devices 13006 areoperating). The data capture devices 15002 may capture images of thedevices 13006 at any suitable interval during the period. For example,the data capture devices 15002 may capture an image once per second,once per a fraction of a second, or any suitable interval during theperiod. In embodiments, the data capture devices 15002 may capture rawimage data. Raw image data may include a signal image, a partial image,data points that represent an image, or other suitable raw image data.In embodiments, the data capture devices 15002 may encode the raw imagedata using any suitable image encode techniques.

The data capture devices 15002 may include cameras, sensors, other imagecapture devices, other data capture devices, or a combination thereof.In embodiments, the data capture devices 15002 may include one or morefull spectrum cameras configured to capture image data that includesvisible light image data and/or non-visible light image data, includinginfrared image data, ultraviolet image data, other non-visible imagedata, or a combination thereof. In embodiments, the data capture devices15002 may include one or more radiation imaging devices, such as anX-ray imaging device or other suitable radiation imaging device. The oneor more radiation imaging devices may be configured to capture imagedata of the devices 13006 during operation of the devices 13006 usingX-ray imaging or other suitable radiation imaging. In embodiments, thedata capture devices 15002 may include one or more sonic capture deviceconfigured to capture image data of the devices 13006 during operationof the devices 13006 using sound waves, such as ultrasonic sound wavesor other suitable sound waves. In embodiments, the data capture devices15002 may include a light imaging, detection, and ranging (LIDAR) deviceconfigured to capture image data of the devices 13006 during operationof the devices 13006 by measuring the distance to a target byilluminating the target with a pulsed light and measuring the reflectedpulses with one or more sensors. In embodiments, the data capturedevices 15002 may include a point cloud data capture device configuredto capture image data of the devices 13006 during operation of thedevices 13006 using lasers or other suitable light to generate a set ofdata points represent a 3-dimensional model of the devices 13006.

In embodiments, the data capture devices 15002 may include an infraredinspection device configured to capture image data of the devices 13006during operation of the devices 13006 using infrared imaging. Inembodiments, the data capture devices 15002 may include a digital imagecapturing device, such as a digital camera, configured to capture imagedata of the devices 13006 during operation of the devices 13006 usingvisible light. For example, an operator operating, supervising,monitoring, and/or inspecting one or more of the devices 13006 mayutilize a mobile device, such as a mobile phone, smart phone, tabletcomputer, or other suitable mobile device. The mobile device may includean image capture device, such as a digital camera. The operator maycapture image data associated with the image capture device of themobile device. In embodiments, the data capture device 15002 may be astand-alone device that captures image data, as described, andcommunicates the captured image data to a client, a server, or acombination thereof, as will be described.

In embodiments, one or more data capture devices 15002 may be positionedat or near a respective device 13006 at predefined distances andlocations with respect to the respective device 13006. The predefineddistances and locations at which the one or more data capture devices15002 are positioned, or disposed, may be selected such that the one ormore of the data capture devices 15002 has a desired field of datacapture of a point of interest of the respective device 13006. The pointof interested may include any suitable point or areas of the respectivedevice 13006. For example, the point of interest may include a belt,bearing, blade, vane, fan, or any other suitable component, point orarea of interest on or related to the respective device 13006. The fieldof data capture may include a field of vision for an image data capturedevice 15002, a field of sonic data capture for a sonic data capturedevice 15002, or other suitable field of data capture. The data capturedfrom the combine fields of data capture from each respective datacapture device positioned at or near the respective device 13006 may beused, as will be described, by the image data set generator 15006 togenerate one or more image data sets that represent images of the pointof interest of the respective device 13006. In embodiments, the datacapture devices 15002 may include any combination of the devicesdescribed herein or other suitable data capture devices not described.

In embodiments, the data capture devices 15002 may capture image data ofthe devices 13006, as described, and communicate the captured image datato a client 15004 and/or a server 15010 using a network 15008. Theclient 15004 may include any suitable client including those describedthroughout this disclosure. In embodiments, the client 15004 may be amobile device, or other suitable client. The client may include aprocessor configured to execute instructions (e.g., instructions that,when executed by the processor, cause the processor to execute variousportions of the computer vision system 15000 or various methodsdescribed herein) stored on a memory. The client 15004 may be owned,operated, and/or utilized by an operator working on or near the devices13006, as described throughout this disclosure. The network 15008 may beany suitable network, including any network described throughout thisdisclosure, including, but not limited to, the Internet, a cloudnetwork, a local area network, a wide area network, a wireless network,a wired network, a cellular network, and the like, or any combinationthereof. The server 15010 may be any suitable server, including anyserver described throughout this disclosure. The server 15010 mayinclude a processor configured to execute instructions (e.g.,instructions that, when executed by the processor, cause the processorto execute various portions of the computer vision system 15000 orvarious methods described herein) stored on a memory. The server 15010may be a stand-alone server or a group of servers. The server 15010 maybe a dedicated server or one of a distributed computing servers or acloud server, and the like, or any combination thereof.

In embodiments, the computer vision system 15000 may include an imagedata set generator 15006. The image data set generator 15006 maycomprise an application or other suitable software or program capable ofbeing executed on the client 15004 and/or the server 15010. Inembodiments, the client 15004 may be configured to execute the imagedata set generator 15006. For example, an operator, as described, maycarry the client 15004 as the operator interacts with a first devices13006. One or more of the data capture devices 15002 may be configuredto capture image data, as described, associated with the first device13006. For example, a first data capture device 15002 may be disposednear the first device 13006, such that, the first data capture device15002 has a field of data capture, as described, to a point of intereston the first device 13006. The first data capture device 15002 maycapture raw image data associated with the first device 13006. The firstdata capture device 15002 may communicate, via the network 15008, theraw image data to the client 15004. The image data set generator 15006may generate one or more image data sets, as will be described, usingthe raw image data. In some embodiments, the server 15010 may beconfigured to execute the image data set generator 15006, as isgenerally illustrated in FIG. 298. The first data capture device 15002may communicate, via the network 15008, the raw image data to the server15010. The image data set generator 15006, being executed by the server15010, may generate one or more image data sets, as will be described,using the raw image data.

In embodiments, the image data set generator 15006 may be configured togenerate one or more image data sets using raw image data received fromthe one or more data capture devices 15002. The image data sets mayinclude images that include data capable (e.g., in a suitable format) ofbeing analyzed or processed by the vision analytics module 15012, aswill be described. The image data set generator 15006 may be configuredto decode raw image data. For example, as described, the one or moredata capture devices 15002 may encode raw image data beforecommunicating the encoded raw image data to the client 15004 and/or theserver 15010. The image data set generator 15006 may be configured todecode the raw image data using any suitable image decoding techniques.In some embodiments, the image data set generator 15006 may beconfigured to correlate related raw image data, stitch raw image data(e.g., by using multiple images from one or more data capture devices15002 to create a single image of a point of interest on one of thedevices 13006), or generate image data sets using any suitable imagedata set generation techniques, and/or any suitable image processingtechniques.

In embodiments, the image data set generator 15006 may generate theimage data sets from raw data comprising data other than visible lightimage data. For example, as described, the data capture devices 15002may capture data such as sonic data, non-visible light data, and othervarious data. The image data set generator 15006 may receive thenon-image raw data and convert the non-image raw data into image data.For example, the image data set generator 15006 may generate one or moreimages of the point of interest of the device 13006 using sound wavescaptured by one or more data capture devices 15002. The image data setgenerator 15006 may generate the image data set using any suitabletechnique. The image data set generator 15006 may communicate the one ormore image data sets to a vision analytics module 15012.

In embodiments, the vision analytics module 15012 may be an applicationor other suitable software capable of being executed on the server15010. While the vision analytics module 15012 is illustrated anddescribed as being executed by the server 15010, it should be understoodthat the client 15004 may be configured to execute the vision analyticsmodule 15012.

As is generally illustrated in FIG. 299, the vision analytics module15012 may include an image data database 15014, a training data database15016, a visual analyzer 15018, and an operating characteristicsdetector 15020. In embodiments, the image data databased 15014 mayinclude any suitable database and may be disposed locally on the client15004 and/or the server 15010, remotely from either of the client 15004and the server 15010, or other suitable location. The image datadatabase 15014 may store the image data sets generated by the image dataset generator 15006, as described. For example, the image data setgenerator 15006 may generate one or more image data sets, as described,and communicate the one or more image data sets to the image datadatabase 15014. In embodiments, the image data database 15014 may be anysuitable image repository configured to store the image data sets.

The training data database 15016 may include any suitable database andmay be disposed locally on the client 15004 and/or the server 15010,remotely from either of the client 15004 and the server 15010, or othersuitable location. The training data database 15016 may store thetraining data sets generated by a deep learning system, as will bedescribed. In embodiments, the training data database 15016 may be anysuitable training data repository configured to store the training datasets. The training data sets may include any suitable training datasets. For example, the training data sets may be generated by a deeplearning system, as will be described, using various suitable image datasets, such as image data sets representing portions of the devices13006, portions of other devices, image data sets representing motion,vibration, or other various characteristics of the devices 13006 orother devices, or any other suitable image data sets or other data sets.

In embodiments, the training data sets may be used to train the computervision system 15000 to detect the various operating characteristics ofthe devices 13006. For example, as will be described, the deep learningsystem may train the visual analyzer 15018 to identify various datapoints of the image data sets, such as, anomalies, features,characteristics, or other suitable data points. In embodiments, thevisual analyzer 15018 may be trained by any suitable training system,such as a machine learning system, an artificial intelligence trainingsystem, deep learning system, programmed by a human programmer, orconfigured, trained, programmed, etc. using any suitable techniques,methods, and/or systems. For example, the visual analyzer 15018 may beconfigured to identify a portion of a point of interest of a respectivedevice 13006 represented in an image data set. For example, the visualanalyzer 15018 may identify a portion of a belt of the respective device13006 represented by the image data set. The visual analyzer 15018 maybe configured to analyze the portion of the point of interest anddetermine whether the characteristics (e.g., position, size, shape,and/or other suitable characteristics) of the portion of the point ofinterest corresponds to predicted or predetermined characteristics ofthe portion of the point of interest. For example, the visual analyzer15018 may identify the portion of the point of interest in one of aplurality of images associated with the image data set. The visualanalyzer 15018 may record values corresponding to variouscharacteristics of the portion of the point of interest associated witheach of the plurality of images of the image data set. For example, thevisual analyzer 15018 may record a position of a portion of a belt ofthe respective device 13006 in each image of the plurality of successiveimages of the image data set and may track the delta in the position ofthe belt in the successive images.

The predicted or predetermined characteristics may be predicted orpredetermined based on the training data sets and may correspond tocharacteristics of the portion for the point of interest where theportion of the point of interest indicates that the respective device13006 is operating within acceptable or expected tolerances. Forexample, the predicted or predetermined characteristics of the portionof the point of interest may include a position of a portion of a beltwhile the respective device 13006 is operating. The position of the beltmay correspond to an expected operating position of the belt while therespective device 13006 is operating (e.g., where the portion of thebelt is expected to be while the respective device 13006 is operatingaccording to acceptable operating tolerances). While various examplesare described, it should be understood that the visual analyzer 15018may use any suitable characteristics of the portion of the point ofinterest to analyze the image data sets.

In embodiments, the visual analyzer 15018 may compare the recordedcharacteristics of the portion of the point of interest with thepredicted or predetermined characteristics of the portion of the pointof interest. The visual analyzer 15018 may be configured (e.g., trained,configured, programmed, etc., as described above), to generate analyticsof the portion of the point of interest based on the comparison of therecorded characteristics of the portion of the point of interest withthe predicted or predetermined characteristics of the portion of thepoint of interest. For example, the visual analyzer 15018 may determinea variance between a recorded position of the portion of the point ofinterest and a predicted or predetermined position of the portion of thepoint of interest (e.g., a variance between an actual or observedposition of, for example, the belt of the respective device 13006 apredicted or predetermined position of the belt of the respective device13006). As described, the image data set may include a plurality ofimages of the portion of the point of interest captured over a period.The visual analyzer 15018 may determine a first variance between a firstrecorded characteristic of the portion of the point of interest and afirst predicted or predetermined characteristic of the portion of thepoint of interest at a first interval during the period (e.g., using afirst image captured during the first interval). The visual analyzer15018 may then determine a second variance between a second recordedcharacteristic of the portion of the point of interest and a secondpredicted or predetermined characteristic of the portion of the point ofinterest at a second interval during the period (e.g., using a secondimage captured during the second interval). The visual analyzer 15018may continue to determine variances for a plurality of recordedcharacteristics and a plurality of predicted or predeterminedcharacteristics over the period using images corresponding to intervalsduring the period. In this manner, the visual analyzer 15018 maygenerate data that represents the variance of the characteristics of theportion of the point of interest with respect to the predicted orpredetermined characteristics of the portion of the point of interestovertime. For example, the visual analyzer 15018 may generate data thatrepresents the difference in the actual or observed position of the beltcompared to the predicted or predetermined position of the belt over aperiod of time. The visual analyzer 15018 may quantize the variance. Forexample, the visual analyzer 15018 may be configured to determine avalue representing the variance between the recorded characteristics andthe predicted or predetermined characteristics (e.g., a valuerepresenting a distance between a recorded position of the belt and apredicted or predetermined position of the belt). In embodiments, thevisual analyzer 15018 may be configured to generate a variance data setthat includes values representing the variances between the recordedcharacteristics of the portion of the point of interest and thepredicted or predetermined portion of the point of interest. The visualanalyzer 15018 may communicate the variance data set to the operatingcharacteristics detector 15020.

In embodiments, the operating characteristics detector 15020 may belocated or disposed on the vision analytics module 15012 or located ordisposed remotely from the vision analytics module 15012. Inembodiments, the operating characteristics detector 15020 may beconfigured to determine or identify various operating characteristics ofthe respective device 13006, or any suitable device 13006, based on thevariance data set. The various operating characteristics may includevibration, heat, distortion, deflection, other suitable operatingcharacteristics, or a combination thereof of the portion of the point ofinterest during operating of the respective device 13006, vibration,heat, distortion, deflection, other suitable operating characteristics,or a combination thereof of other portions of the respective device13006, other suitable operating characteristics of the respective device13006, or a combination thereof. As described, the operatingcharacteristics detector 15020 may be trained by any suitable trainingsystem, such as a machine learning system, an artificial intelligencetraining system, deep learning system, programmed by a human programmer,or configured, trained, programmed, etc. using any suitable techniques,methods, and/or systems. In embodiments, the operating characteristicsdetector 15020 may be configured to identify operating characteristicsof the portion of the point of interest by identifying various data ofthe variance data set that indicate quantities or other suitablemeasurements of one or more operating characteristics of the respectivedevice 13006.

For example, the operating characteristics detector 15020 may identifydata of the variance data set that indicates that the belt is vibratingat a first frequency (e.g., by identifying values associated with thevariance data set that indicate that the position of the belt over aperiod of time is moving at a first frequency). The operatingcharacteristics detector 15020 may compare the identified operatingcharacteristics with trained or programmed operating characteristics todetermine whether the operating characteristics are within operatingtolerance for the respective device 13006. For example, the operatingcharacteristics detector 15020 may compare a value associated with theoperating characteristic with a threshold value (e.g., and determinewhether the operating characteristic is within tolerances depending onwhether the operating characteristic value is above or below thethreshold), compare the value associated with the operatingcharacteristic to a predicted value (e.g., and determine if the valuesare different that the operating characteristic is not operating withintolerances), or other suitable determinative analysis, or a combinationthereof. For example, the operating characteristics detector 15020 maycompare the frequency at which the belt is vibrating with a trained orprogrammed frequency. The trained or programmed frequency may include afrequency of vibration of the belt during normal or acceptable operationof the respective device 13006, a frequency of vibration of the beltthat indicates the belt is vibrating beyond acceptable tolerances, afrequency of vibration that is within the normal or acceptable operationof the respective device 13006 and indicates that the belt mayeventually vibrate at a frequency beyond the acceptable tolerances ofthe operation of the respective device 13006, or other suitablefrequencies. While only vibration is described, the trained orprogrammed operating characteristics may indicate any suitable operatingcharacteristics of the respective device 13006. The operatingcharacteristics detector 15020 may output (e.g., to a database, to areport, to monitor, or other suitable output location or device) anoperatic characteristics data set that includes data indicating valuesor the operating characteristics and/or information indicatingpredictive (e.g., future) operating characteristics (e.g., determinedbased on the actual or observed operating characteristics of the portionof the point of interest and the trained or programmed operatingcharacteristic that indicate that the actual or observed operatingcharacteristics indicate particular further operating characteristics),actual or observed operating characteristics, other suitable informationor values, or a combination thereof.

In embodiments, an operator may review and/or analyze the operatingcharacteristics data set to determine whether the respective device13006, and/or the portion of the point of interest of the respectivedevice 13006, is operating within expected or acceptable tolerances.Additionally, or alternatively, the operator may determine, based on theoperating characteristics data set that one or more components of therespective device 13006 is faulty, will become faulty, requiresmaintenance, or other suitable determinations. For example, theoperating characteristics data set may indicate that the belt isvibrating at a first frequency. The belt vibrating at the firstfrequency may indicate that a pulley associated with the belt is faultyor requires maintenance. The operator may maintain or replace the pulleybased on the operating characteristics data. In embodiments, theoperating characteristics detector 15020 may be configured to outputinformation or data that indicates that a component of the respectivedevice 13006 requires maintenance or replacement. For example, asdescribed, the operating characteristics data set may indicate that thebelt is vibrating at the first frequency. The operating characteristicsdetector 15020 may be configured to determine, based on the operatingcharacteristics data set (e.g., indicating that the belt is vibrating atthe first frequency), and the trained or programmed operatingcharacteristics that the belt vibrating at the first frequency indicatesthat a first pulley is faulty and should be replaced or maintained. Theoperating characteristics detector 15020 may output the information ordata to the operator, as described, who may then act on the informationor data (e.g., by replacing or maintaining the first pulley).

In embodiments, the computer vision system 15000 may capture data fromthe respective devices 13006 (e.g., non-image data), using variousnon-visual input devices. For example, the data capture devices 15002may capture data, such as temperature, pressure, chemical structure,other suitable non-visual data, or a combination thereof, duringoperation of the respective devices 13006. A chemical structure mayinclude a molecular geometry representing spatial arrangements of atomsin a molecular and the chemical bonds that hold the atoms together. Achemical structure can be represented by molecular models or formulas.For example, the data captures devices 15002 may capture a plurality ofmeasurement values over a period of time (e.g., during which therespective devices 13006 are operating). The data capture devices 15002may capture measurements of the respective devices 13006 at any suitableinterval during the period. For example, the data capture devices 15002may capture a measurement once per second, once per a fraction of asecond, or any suitable interval during the period. In embodiments, thedata capture devices 15002 may capture raw measurement data. Rawmeasurement data may include a temperature measurement, a pressuremeasurement (e.g., of liquid or gas within a portion of the respectivedevice 13006), a chemical structure measurement (e.g., of a liquid, gas,or solid within a portion of the respective device 13006), or othersuitable raw measurement data. In embodiments, the data capture devices15002 may encode the raw measurement data using any suitable measurementencoding techniques.

The data capture devices 15002 may include pressure sensors, temperaturesensors, chemical sensors, fluid sensors, other sensors, other datacapture devices, or a combination thereof. In embodiments, the datacapture devices 15002 may include one or more pressure sensorsconfigured to capture pressure measurement data that includes of aportion of the respective device 13006. For example, a pressure sensormay measure pressure within a vat, pipe, tank, or other suitablepressurized enclosure of the respective device 13006. In embodiments,the data capture devices 15002 may include one or more temperaturesensors configured to measure temperature of a portion of the respectivedevice 13006. For example, a temperature sensor may measure temperatureof oven, kiln, vat, pipe, tank, or other suitable portions of therespective device 13006. In embodiments, the data capture devices 15002may include one or more chemical sensors configured to measure ordetermine a chemical structure of a liquid, gas, or solid associatedwith the respective device 13006. For example, a chemical sensor maymeasure the chemical structure of a part manufactured by the respectivedevice 13006, the chemical structure of cooling fluid used to cool therespective device 13006 during operation, the chemical structure ofwaste produced by the respective device 13006 during operation, or othersuitable chemical structures of other suitable liquids, fluids, gases,or solids associated with the respective device 13006.

In embodiments, the data capture devices 15002 may be associated with amobile device. For example, an operator operating, supervising,monitoring, and/or inspecting one or more of the respective devices13006 may utilize a mobile device, such as a mobile phone, smart phone,tablet computer, or other suitable mobile device. The mobile device mayinclude a data capture device, such as an add-on sensor. The operatormay capture measurement data using the add-on sensor of the mobiledevice. In embodiments, the data capture device 15002 may be astand-alone device that captures measurement data, as described, andcommunicates the captured measurement data to the client 15004, theserver 15010, or a combination thereof, as described.

In embodiments, one or more data capture devices 15002 may be positionedat or near a respective device 13006 at predefined distances andlocations with respect to the respective device 13006. The predefineddistances and locations at which the one or more data capture devices15002 are positioned, or disposed, may be selected such that the one ormore data capture devices 15002 has a desired field of data capture of apoint of interest of the respective device 13006. As described, thepoint of interested may include any suitable point or areas of therespective device 13006. For example, the point of interested mayinclude a vat, tank, pipe, enclosure, manufactured part, coolant fluid,waste product, other suitable points of interest, or a combinationthereof. The field of data capture may include an area in which thedesired measurement can be captured using the data capture devices15002. The data captured from the combine fields of data capture fromeach respective data capture device 15002 positioned at or near therespective device 13006 may be used, as described, by the image data setgenerator 15006 to generate one or more image data sets that representimages of the point of interest of the respective device 13006. Inembodiments, the data capture devices 15002 may include any combinationof the devices described herein or other suitable data capture devicesnot described.

In embodiments, the data capture devices 15002 may capture measurementdata of the respective devices 13006, as described, and communicate thecaptured measurement data to the client 15004 and/or the server 15010using the network 15008. The client 15004 may include any suitableclient including those described throughout this disclosure. Inembodiments, the client 15004 may be a mobile device, or other suitableclient. The client 15004 may be owned, operated, and/or utilized by anoperator working on or near the respective devices 13006, as describedthroughout this disclosure. The network 15008 may be any suitablenetwork, including any network described throughout this disclosure,including, but not limited to, the Internet, a cloud network, a localarea network, a wide area network, a wireless network, a wired network,a cellular network, and the like, or any combination thereof. The server15010 may be any suitable server, including any server describedthroughout this disclosure. The server 15010 may be a stand-alone serveror a group of servers. The server 15010 may be a dedicated server or oneof a distributed computing servers or a cloud server, and the like, orany combination thereof.

In embodiments, as described, the image data set generator 15006 maycomprise an application or other suitable software or program capable ofbeing executed on the client 15004 and/or the server 15010. Inembodiments, the client 15004 may be configured to execute the imagedata set generator 15006. For example, an operator, as described, maycarry the client 15004 as the operator interacts with a first devices13006. One or more of the data capture devices 15002 may be configuredto capture measurement data, as described, associated with the firstdevice 13006. For example, a first data capture device 15002 may bedisposed near the first device 13006, such that, the first data capturedevice 15002 has a field of data capture, as described, to a point ofinterest on the first device 13006. The first data capture device 15002may capture raw measurement data associated with the first device 13006.The first data capture device 15002 may communicate, via the network15008, the raw measurement data to the client 15004. The image data setgenerator 15006 may generate one or more image data sets using the rawmeasurement data. In some embodiments, the server 15010 may beconfigured to execute the image data set generator 15006, as isgenerally illustrated in FIG. 298. The first data capture device 15002may communicate, via the network 15008, the raw measurement data to theserver 15010. The image data set generator 15006, being executed by theserver 15010, may generate one or more image data sets using the rawmeasurement data.

In embodiments, the image data set generator 15006 may be configured togenerate one or more image data sets using raw measurement data receivedfrom the one or more data capture devices 15002. The image data sets mayinclude images that include data capable (e.g., in a suitable format) ofbeing analyzed or processed by the vision analytics module 15012, asdescribed. The image data set generator 15006 may be configured todecode raw measurement data. For example, as described, the one or moredata capture devices 15002 may encode raw measurement data beforecommunicating the encoded raw measurement data to the client 15004and/or the server 15010. The image data set generator 15006 may beconfigured to decode the raw measurement data using any suitablemeasurement decoding techniques. For example, the image data setgenerator 15006 may be configured to interpret a signal representing ameasured value as the measurement value. In some embodiments, the imagedata set generator 15006 may be configured to correlate related rawmeasurement data, stitch raw measurement data (e.g., by using multiplemeasurements from one or more data capture devices 15002 to create asingle value that represents a point of interest on one of therespective devices 13006), or generate image data sets using anysuitable image data set generation techniques, and/or any suitablemeasurement data processing techniques. For example, the image data setgenerator 15006 may be configured to use measurement data correspondingto pressure, temperature, chemical structure, or other suitablemeasurement data, to generate image data that represents the point ofinterest of the respective device 13006.

In embodiments, the image data set generator 15006 may be configured touse measurement data, as described, in combination with raw image data(e.g., captured by the data capture devices 15002, as described above),to generate one more image data sets. For example, the image data setgenerator 15006 may be configured to generate an image of the point ofinterest of the respective device 13006 using captured image datacombined with an associated temperature measurement to generate aprecise image of the point of interest (e.g., accounting for, forexample, component expansion, deflection, growth, shrinkage, or otherchange in shape or size due to the temperature of the component). Theimage data set generator 15006 may communicate the one or more imagedata sets to a vision analytics module 15012. In embodiments, the visionanalytics module 15012 may be an application or other suitable softwarecapable of being executed on the server 15010. While the visionanalytics module 15012 is illustrated and described as being executed bythe server 15010, it should be understood that the client 15004 may beconfigured to execute the vision analytics module 15012. In embodiments,the vision analytics module 15012 may analyze the image data sets, asdescribed. For example, the visual analyzer 15018 may analyze the imagedata sets. The operating characteristics detector 15020 may identifyoperating characteristics, as described.

In embodiments, as described, the training data database 15016 mayinclude any suitable database and may be disposed locally on the client15004 and/or the server 15010, remotely from either of the client 15004and the server 15010, or other suitable location. The training datadatabase 15016 may store the training data sets generated by a deeplearning system, as will be described. In embodiments, the training datadatabase 15016 may be any suitable training data repository configuredto store the training data sets. The training data sets may include anysuitable training data sets. For example, the training data sets may begenerated by a deep learning system, as will be described, using varioussuitable data sets, such as data sets representing portions of therespective devices 13006, portions of other devices, data setsrepresenting pressure, data sets representing temperature, data setsrepresenting chemical structure, data sets representing vibration, orother various characteristics of the respective devices 13006 or otherdevices, or any other suitable data sets.

In embodiments, the training data sets may be used to train the computervision system 15000 to detect the various operating characteristics ofthe respective devices 13006. For example, as will be described, thedeep learning system may train the visual analyzer 15018 to identifyvarious data points of the image data sets, such as, anomalies,features, characteristics, or other suitable data points. Inembodiments, the visual analyzer 15018 may be trained by any suitabletraining system, such as a machine learning system, an artificialintelligence training system, deep learning system, programmed by ahuman programmer, or configured, trained, programmed, etc. using anysuitable techniques, methods, and/or systems. For example, the visualanalyzer 15018 may be configured to identify a portion of a point ofinterest of the respective device 13006 represented in an image dataset. For example, the visual analyzer 15018 may identify a portion of abelt of the respective device 13006 represented by the image data set.The visual analyzer 15018 may be configured to analyze the portion ofthe point of interest and determine whether the characteristics (e.g.,position, size, shape, and/or other suitable characteristics) of theportion of the point of interest corresponds to predicted orpredetermined characteristics of the portion of the point of interest.For example, the visual analyzer 15018 may identify the portion of thepoint of interest in one of a plurality of images associated with theimage data set. The visual analyzer 15018 may record variouscharacteristics of the portion of the point of interest associated witheach of the plurality of images of the image data set. For example, thevisual analyzer 15018 may record a pressure value, a temperature value,or other suitable measured value associated with a portion of a belt ofthe respective device 13006 in each image of the plurality of successiveimages of the image data set and may track the delta in the measuredvalues of the belt in the successive images (e.g., using the measuredvalues captured by the data capture devices 15002, as described). Asdescribed, the visual analyzer 15018 may generate variance data setsbased on the deltas between the recorded values and the predicted orpredetermined values.

In embodiments, the operating characteristics detector 15020 may belocated or disposed on the vision analytics module 15012 or located ordisposed remotely from the vision analytics module 15012. Inembodiments, the operating characteristics detector 15020 may beconfigured to determine or identify various operating characteristics ofthe respective device 13006, or any suitable respective device 13006,based on the variance data set. The various operating characteristicsmay include vibration, heat, distortion, deflection, other suitableoperating characteristics, or a combination thereof of the portion ofthe point of interest during operating of the respective device 13006,vibration, heat, distortion, deflection, other suitable operatingcharacteristics, or a combination thereof of other portions of therespective device 13006, other suitable operating characteristics of therespective device 13006, or a combination thereof.

As described, the operating characteristics detector 15020 may betrained by any suitable training system, such as a machine learningsystem, an artificial intelligence training system, deep learningsystem, programmed by a human programmer, or configured, trained,programmed, etc. using any suitable techniques, methods, and/or systems.In embodiments, the operating characteristics detector 15020 may betrained by a deep learning system, as will be described, using thetraining data sets that include data sets representing portions of therespective devices 13006, portions of other devices, data setsrepresenting pressure, data sets representing temperature, data setsrepresenting chemical structure, data sets representing vibration, orother various characteristics of the respective devices 13006 or otherdevices, or any other suitable data sets. In embodiments, the operatingcharacteristics detector 15020 may be configured to identify operatingcharacteristics of the portion of the point of interest by identifyingvarious data of the variance data set that indicate quantities or othersuitable measurements of one or more operating characteristics of therespective device 13006. In embodiments, the operating characteristicsmay include a pressure within a component of the respective device13006, a temperature of at least a portion of a component of therespective device 13006, a chemical structure of a material (e.g., gas,liquid, or solid of or within a component of the respective device 13006or of a component or part manufactured by the respective device 13006),a density of a material (e.g., gas, liquid, or solid of or within acomponent of the respective device 13006 or of a component or partmanufactured by the respective device 13006), other suitable operatingcharacteristics, or a combination thereof.

For example, the operating characteristics detector 15020 may identifydata of the variance data set that indicates that a component of therespective device 13006 is misshapen due to an unexpected increase intemperature (e.g., by identifying values associated with the variancedata set that indicate that the temperature of the component over aperiod of time is increasing at a rate greater than expected). Theoperating characteristics detector 15020 may compare the identifiedoperating characteristics with trained or programmed operatingcharacteristics to determine whether the operating characteristics arewithin operating tolerance for the respective device 13006. For example,the operating characteristics detector 15020 may compare the rate oftemperature change of the component with a trained or programmed rate oftemperature change of the component. The operating characteristicsdetector 15020 may output (e.g., to a database, to a report, to monitor,or other suitable output location or device) an operatic characteristicsdata set that includes data indicating values or the operatingcharacteristics and/or information indicating predictive (e.g., future)operating characteristics (e.g., determined based on the actual orobserved operating characteristics of the portion of the point ofinterest and the trained or programmed operating characteristic thatindicate that the actual or observed operating characteristics indicateparticular further operating characteristics), actual or observedoperating characteristics, other suitable information or values, or acombination thereof. As described, an operator may analyze the outputdata and take appropriate corrective action. Additionally, oralternatively, the computer vision system 15000 may automaticallyidentify a corrective action and initiate the corrective action.

In embodiments, the computer vision system 15000 may implement aclassification model (e.g., using a deep neural network, or othersuitable neural or other networks). For example, the vision analyticsmodule 15012 may implement a classification module that receivesanalytics of the image data, including the variance data sets describedabove. The vision analytics module 15012 may output a classificationrelating to an operating characteristic of the respective device 13006.For example, the classification model, via the vision analytics module15012, may receive features defining the variances between the recordedcharacteristics of the image data sets of the belt of the respectivedevice 13006, in operation. The classification model, having beentrained using image data and/or non-image data corresponding to faultybelts, image data and/or non-image data corresponding to belts not yetfaulty, and image and/or non-image data corresponding to belts operatingin an expected and/or acceptable condition, may output a classificationthat indicates whether the belt is faulty, operating within expectedand/or acceptable condition but trending towards faulty, or in expectedand/or acceptable operating condition.

In embodiments, the operating characteristics detector 15020, the visionanalytics module 15012, and/or the computer vision system 15000 maygenerate one or more warnings, signals, indicators, or other suitableoutputs configured to alert the operator of one or more of the operatingcharacteristics of the respective device 13006, of one or morecomponents of the respective device 13006 that requires maintenance orreplacement, any other suitable alert, or a combination thereof. Forexample, the computer vision system 15000 may be configured to generatea message, such as a text message, email message, popup message, orother suitable message, indicating that a component (e.g., the firstpulley) of the respective device 13006 requires maintenance. The messagemay include text, characters, images, or other suitable information thatconveys the intend message. The computer vision system 15000 may beconfigured to communicate, via the network 15008, near fieldcommunication, or other suitable communication system or protocol, themessage to the operator. For example, the computer vision system 15000may communicate the message to a mobile device, as described, or othersuitable device and/or location.

In embodiments, the computer vision system 15000 may be configured todisplay on an output display a current status of one or more respectivedevices 13006. For example, a factory, plant, or other suitable locationof the respective devices 13006 may include an output display (e.g., ascreen or monitor) located such that operators within proximity of therespective devices 13006 can see the output display. The computer visionsystem 15000 may be configured to display a status (e.g., a red, yellow,green status, an up or down status, or other suitable status orindicator, or a combination thereof) of one or more of the respectivedevices 13006. For example, the computer vision system 15000 may displaya green status next to the respective device 13006 that is operatingwithin tolerable operating conditions (e.g., based on the visualanalysis of the image data sets described above). In another example,the computer vision system 15000 may display a yellow status next to therespective device 13006 that is operating within tolerable operatingconditions and the visual analysis indicates that the respective device13006 may start to operated outside of the tolerable operatingconditions if the operating characteristics (e.g., identified, asdescribed) continue along a current operating trend (e.g., based on thefrequency of vibration of the belt, the computer vision system 15000determines that continued vibration at that frequency and/or increasedfrequency may cause the respective device 13006 to operate outside ofthe tolerable operating conditions). In another example, the computervision system 15000 may display a red status next to the respectivedevice 13006 that is currently operating outside of tolerable operatingconditions. In embodiments, the computer vision system 15000 may displaythe operating status of the respective devices 13006 on other suitabledisplays, such as a display of a mobile device, as described. Forexample, the mobile device may include an application that displays theoperating status of the respective devices 13006.

In embodiments, the output of the vision analytics module 15012 may beused to updated and/or improve the training data sets, described above.For example, output from the vision analytics module 15012 may be usedto update the training data sets to include additional operatingcharacteristics, improve the precision of the values used to predictvarious operating characteristics, used for other suitable updates orimprovements to the training data sets, or a combination thereof. Thetraining data sets may be used as a continuous feedback to the computervision system 15000 to improve predictive and determinative capabilitiesof the computer vision system 15000.

In embodiments, the output of the vision analytics module 15012 may beused to populate and/or update a knowledgebase that may be used by anoperator or by the computer vision system 15000 to identify faults,schedule repairs or maintenance, adjust settings on the respectivedevices 13006, take other corrective action, or other suitable action.For example, the output of the vision analytics module 15012 may becorrelated with a corresponding repair of a component (e.g., the outputof the vision analytics module 15012 may indicate that vibration of thebelt is beyond the expected or acceptable tolerance and an operator mayhave replaced a pulley in response to the output). The knowledgebase maybe updated to indicate that the output of the vision analytics module15012 (e.g., including the values of the operating characteristicsdetermined above) resulted in a replaced pulley. In this manner, theknowledgebase may continue to grow and provide accurate and preciseinformation for an operator or the computer vision system 15000 as itrelates to operating characteristics and corresponding correctiveactions, thereby improving the efficiency of the computer vision system15000 and assisting the operator in identifying issues and correspondingcorrective actions.

In embodiments, the computer vision system 15000 may be configured tovisually inspect components, parts, systems, devices, or a combinationthereof, other than those described above. For example, the computervision system 15000 may be configured to visually inspect, as described,parts manufactured in a parts manufacturing facility. For example, thedata capture devices 15002 may be disposed or positioned such that fieldof data capture for each respective data capture device 15002 isdirected toward at least a portion of a part being manufactured (e.g.,on a parts manufacturing line). The data capture devices 15002 maycapture data associated with the parts as the parts move along the partsmanufacturing line. The computer vision system 15000 may analyze thedata captured by the data capture devices 15002 (e.g., as image datasets generated by the image data set generator 15006) and identifyanomalies, variations, or other conditions that deviate from tolerablestandards for the part. In embodiments, the part may include a part fora vehicle, a part for a bike, a bike chain, a gasket, a fastener (e.g.,a screw, a bolt, a nut, a nail, and the like), a printed circuit board,a capacitor, an inductor, a resistor, or other suitable part. Forexample, the computer vision system 15000 may analyze image data setsassociated with bike chains being manufactured. The computer visionsystem 15000 may identify a bend in a portion of a bike chain that isoutside of the tolerable standards for the portion of the bike chainbased on the analysis described above. The computer vision system 15000may generate a message, as described, indicating that the bike chainshould be taken out of circulation, repaired, destroyed, or othersuitable action.

As is generally illustrated in FIGS. 300-301, a deep learning system15030 may be configured to train the computer vision system 15000, usingthe training data sets, to identify operating characteristics of therespective devices 13006 or other suitable devices, identify correctiveactions in response to the identified operating characteristics, andinitiate corrective action based on the identified corrective actions.The deep learning system 15030 may train the computer vision system15000 using learning based on data representations. In embodiments, thedeep learning system 15030 may train the computer vision system 15000using supervised training (e.g., using classification), semi-supervisedtraining, or unsupervised training (e.g., using pattern analysis). Inembodiments, the deep learning system 15030 may include a deep neuralnetwork, a deep belief network, a recurrent neural network, othersuitable networks or learning systems, or a combination thereof.

In embodiments, the deep learning system 15030 may include propositionalformulas or latent variables organized into a plurality of layers. Eachof the plurality of layers may be configured to represent an abstractportion of an image. For example, a first layer may represent anabstract of pixels and encode edges of an input image, for example, animage representing a point of interest of the representative device13006. A second layer may represent arrangements of the edges. A thirdlayer may encode a first portion of a component within the point ofinterest of the representative device 13006 (e.g., a portion of thebelt, as described). A fourth later may represent another encodedportion of the component, and so on, such that, the plurality of layers,when overlaid, represents the point of interest of the representativedevice 13006. The deep learning system 15030 may be configured totranslate the layers into training data sets, used to train the computervision system 15000. For example, the deep learning system 15030 maytranslate a plurality of layers of one or more images that represents abelt of the representative device 13006 vibrating at a first frequency.The deep learning system 15030 may use input data from various sourcesto determine whether the first frequency represents a frequency at whichthe belt is vibration within the expected or acceptable tolerances, asdescribed. For example, the deep learning system 15030 may receive dataindicating repair data, maintenance data, uptime data, downtime data,profitability data, efficiencies data, operational optimization data,other suitable data, or a combination thereof, associated with therespective device 13006, a process, a production line, a facility, orother suitable systems.

In embodiments, the deep learning system 15030 may identify data valuescorresponding to the first frequency of the belt. For example, the deeplearning system 15030 may identify an uptime value, a downtime value, aprofitability value, other suitable values, or a combination thereofthat correspond to periods when the respective device 13006 operatedwith the belt vibrating at the first frequency. For example, the deeplearning system 15030 may determine that the first frequency is withinthe expected or acceptable tolerances when the data indicates that therespective device 13006 had an uptime that was above a threshold, adowntime that was below a threshold, a profitability that was above athreshold, or a combination thereof. Conversely, the deep learningsystem 15030 may determine that the first frequency is beyond theexpected or acceptable tolerances when, for example, the downtimeassociated with the respective device 13006 was above a threshold. Itshould be understood that the deep learning system 15030 may identifyany suitable operating characteristic besides those disclosed herein andthat the deep learning system 15030 may determine positive or negativeoutcomes of the operating characteristics based on any suitable dataanalysis other than those described herein.

In embodiments, the deep learning system 15030 may generate the trainingdata sets using the identified operating characteristics and associatedanalysis thereof. In embodiments, the deep learning system 15030 maytrain the computer vision system 15000 using the training data sets. Inembodiments, the deep learning system 15030 may receive feedbackinformation from the computer vision system 15000, an operator, aprogrammer, other suitable sources, or a combination thereof. The deeplearning system 15030 may update the training data sets based on thefeedback. For example, the computer vision system 15000, having beentrained using the training data sets, may identify a component asfaulty. The operator may visually inspect the component and determinethat the component is not faulty. The operator and/or the computervision system 15000 may communicate to the deep learning system 15030that the component was not faulty based on the identified operatingcharacteristics (e.g., identified by the computer vision system 15000).The deep learning system 15030 may update the training data sets usingthe feedback from the operator and/or the computer vision system 15000.

In embodiments, an apparatus for detecting operating characteristics ofa manufacturing device includes a memory and a processor. The memoryincludes instructions executable by the processor to generate one ormore image data sets using raw data captured by one or more data capturedevices; identify one or more values corresponding to a portion of themanufacturing device within a point of interest represented by the oneor more image data sets; record the one or more values; compare therecorded one or more values to corresponding predicted values; generatea variance data set based on the comparison of the recorded on or morevalues and the corresponding predicted values; identify an operatingcharacteristic of the manufacturing device based on the variance data;and generate an indication indicating the operating characteristic.

In embodiments, the memory includes instructions further executable bythe processor to identify a corrective action responsive to identifyingthe operating characteristic. In embodiments, the memory includesinstructions further executable by the processor to initiate acorrective action responsive to identifying the operatingcharacteristics. In embodiments, the operating characteristic includes avibration of a component of the manufacturing device. In embodiments,the operating characteristic includes a shape of a component of themanufacturing device. In embodiments, the operating characteristicincludes a size of a component of the manufacturing device. Inembodiments, the operating characteristic includes a deflection of acomponent of the manufacturing device. In embodiments, the operatingcharacteristic includes an electromagnetic emission of a component ofthe manufacturing device. In embodiments, the operating characteristicincludes a temperature of a component of the manufacturing device. Inembodiments, the operating characteristic includes a temperature of agas within a component of the manufacturing device. In embodiments, theoperating characteristic includes a temperature of a liquid within acomponent of the manufacturing device. In embodiments, the operatingcharacteristic includes a temperature of a solid within a component ofthe manufacturing device. In embodiments, the operating characteristicincludes a pressure within a component of the manufacturing device. Inembodiments, the operating characteristic includes a pressure of a gaswithin a component of the manufacturing device. In embodiments, theoperating characteristic includes a pressure of a liquid within acomponent of the manufacturing device. In embodiments, the operatingcharacteristic includes a density of a gas within a component of themanufacturing device.

In embodiments, the operating characteristic includes a density of aliquid within a component of the manufacturing device. In embodiments,the operating characteristic includes a density of a solid within acomponent of the manufacturing device. In embodiments, the operatingcharacteristic includes a density of a component manufactured by themanufacturing device. In embodiments, the component includes a part fora vehicle. In embodiments, the component includes a part for a bike. Inembodiments, the component includes a bike chain. In embodiments, thecomponent includes a gasket. In embodiments, the component includes afastener. In embodiments, the component includes a part for a screw. Inembodiments, the component includes a part for a bolt. In embodiments,the component includes a part for a printed circuit board. Inembodiments, the component includes a part for a capacitor. Inembodiments, the component includes a part for a resistor. Inembodiments, the component includes a part for an inductor. Inembodiments, the operating characteristic includes a chemical structureof a gas within a component of the manufacturing device.

In embodiments, the operating characteristic includes a chemicalstructure of a liquid within a component of the manufacturing device. Inembodiments, the operating characteristic includes a chemical structureof a solid within a component of the manufacturing device. Inembodiments, the operating characteristic includes a chemical structureof a component manufactured by the manufacturing device. In embodiments,the component includes a part for a vehicle. In embodiments, thecomponent includes a part for a bike. In embodiments, the componentincludes a bike chain. In embodiments, the component includes a gasket.In embodiments, the component includes a fastener. In embodiments, thecomponent includes a part for a screw. In embodiments, the componentincludes a part for a bolt. In embodiments, the component includes apart for a printed circuit board. In embodiments, the component includesa part for a capacitor.

In embodiments, the component includes a part for a resistor. Inembodiments, the component includes a part for an inductor. Inembodiments, the data capture device includes an image capture device.In embodiments, the data capture device includes a camera. Inembodiments, the data capture device includes data measurement device.In embodiments, the data capture device includes a sensor. Inembodiments, the data capture device includes a full spectrum camera. Inembodiments, the data capture device includes radiation imaging device.In embodiments, the data capture device includes an X-ray imagingdevice. In embodiments, the data capture device includes a non-visiblelight data capture device. In embodiments, the data capture deviceincludes a visible light data capture device. In embodiments, the datacapture device includes sonic data capture device. In embodiments, thedata capture device includes an image capture device. In embodiments,the data capture device includes light imaging, detection, and rangingdevice. In embodiments, the data capture device includes point clouddata capture device. In embodiments, the data capture device includes aninfrared inspection device. In embodiments, the data capture deviceincludes an image capture device.

In embodiments, the data capture device includes a pressure sensor. Inembodiments, the data capture device includes a temperature sensor. Inembodiments, the data capture device includes a chemical sensor. Inembodiments, the data capture device includes a stand-alone device. Inembodiments, the data capture device includes associated with a mobiledevice. In embodiments, the mobile device includes a smart phone. Inembodiments, the mobile device includes a tablet. In embodiments, theraw data includes raw image data. In embodiments, the raw data includesraw measurement data. In embodiments, the portion of the manufacturingdevice within the point of interest includes a component of themanufacturing device. In embodiments, the portion of the manufacturingdevice within the point of interest includes a belt of the manufacturingdevice. In embodiments, the portion of the manufacturing device withinthe point of interest includes a component manufactured by themanufacturing device. In embodiments, the portion of the manufacturingdevice within the point of interest includes a bike chain manufacturedby the manufacturing device.

In embodiments, a method for detecting operating characteristics of amanufacturing device includes generating one or more image data setsusing raw data captured by one or more data capture devices; identifyingone or more values corresponding to a portion of the manufacturingdevice within a point of interest represented by the one or more imagedata sets; recording the one or more values; comparing the recorded oneor more values to corresponding predicted values; generating a variancedata set based on the comparison of the recorded on or more values andthe corresponding predicted values; identifying an operatingcharacteristic of the manufacturing device based on the variance data;and generating an indication indicating the operating characteristic.

In embodiments, the method also includes identifying a corrective actionresponsive to identifying the operating characteristic. In embodiments,the method also includes initiating a corrective action responsive toidentifying the operating characteristics. In embodiments, the operatingcharacteristic includes a vibration of a component of the manufacturingdevice. In embodiments, the operating characteristic includes a shape ofa component of the manufacturing device. In embodiments, the operatingcharacteristic includes a size of a component of the manufacturingdevice. In embodiments, the operating characteristic includes adeflection of a component of the manufacturing device. In embodiments,the operating characteristic includes an electromagnetic emission of acomponent of the manufacturing device. In embodiments, the operatingcharacteristic includes a temperature of a component of themanufacturing device. In embodiments, the operating characteristicincludes a temperature of a gas within a component of the manufacturingdevice. In embodiments, the operating characteristic includes atemperature of a liquid within a component of the manufacturing device.In embodiments, the operating characteristic includes a temperature of asolid within a component of the manufacturing device. In embodiments,the operating characteristic includes a pressure within a component ofthe manufacturing device. In embodiments, the operating characteristicincludes a pressure of a gas within a component of the manufacturingdevice. In embodiments, the operating characteristic includes a pressureof a liquid within a component of the manufacturing device. Inembodiments, the operating characteristic includes a density of a gaswithin a component of the manufacturing device.

In embodiments, the operating characteristic includes a density of aliquid within a component of the manufacturing device. In embodiments,the operating characteristic includes a density of a solid within acomponent of the manufacturing device. In embodiments, the operatingcharacteristic includes a density of a component manufactured by themanufacturing device. In embodiments, the component includes a part fora vehicle. In embodiments, the component includes a part for a bike. Inembodiments, the component includes a bike chain. In embodiments, thecomponent includes a gasket. In embodiments, the component includes afastener. In embodiments, the component includes a part for a screw. Inembodiments, the component includes a part for a bolt. In embodiments,the component includes a part for a printed circuit board. Inembodiments, the component includes a part for a capacitor. Inembodiments, the component includes a part for a resistor. Inembodiments, the component includes a part for an inductor. Inembodiments, the operating characteristic includes a chemical structureof a gas within a component of the manufacturing device.

In embodiments, the operating characteristic includes a chemicalstructure of a liquid within a component of the manufacturing device. Inembodiments, the operating characteristic includes a chemical structureof a solid within a component of the manufacturing device. Inembodiments, the operating characteristic includes a chemical structureof a component manufactured by the manufacturing device. In embodiments,the component includes a part for a vehicle. In embodiments, thecomponent includes a part for a bike. In embodiments, the componentincludes a bike chain. In embodiments, the component includes a gasket.In embodiments, the component includes a fastener. In embodiments, thecomponent includes a part for a screw. In embodiments, the componentincludes a part for a bolt. In embodiments, the component includes apart for a printed circuit board. In embodiments, the component includesa part for a capacitor.

In embodiments, the component includes a part for a resistor. Inembodiments, the component includes a part for an inductor. Inembodiments, the data capture device includes an image capture device.In embodiments, the data capture device includes a camera. Inembodiments, the data capture device includes data measurement device.In embodiments, the data capture device includes a sensor. Inembodiments, the data capture device includes a full spectrum camera. Inembodiments, the data capture device includes radiation imaging device.In embodiments, the data capture device includes an X-ray imagingdevice. In embodiments, the data capture device includes a non-visiblelight data capture device. In embodiments, the data capture deviceincludes a visible light data capture device. In embodiments, the datacapture device includes sonic data capture device. In embodiments, thedata capture device includes an image capture device. In embodiments,the data capture device includes light imaging, detection, and rangingdevice. In embodiments, the data capture device includes point clouddata capture device. In embodiments, the data capture device includes aninfrared inspection device. In embodiments, the data capture deviceincludes an image capture device.

In embodiments, the data capture device includes a pressure sensor. Inembodiments, the data capture device includes a temperature sensor. Inembodiments, the data capture device includes a chemical sensor. Inembodiments, the data capture device includes a stand-alone device. Inembodiments, the data capture device includes associated with a mobiledevice. In embodiments, the mobile device includes a smart phone. Inembodiments, the mobile device includes a tablet. In embodiments, theraw data includes raw image data. In embodiments, the raw data includesraw measurement data. In embodiments, the portion of the manufacturingdevice within the point of interest includes a component of themanufacturing device. In embodiments, the portion of the manufacturingdevice within the point of interest includes a belt of the manufacturingdevice. In embodiments, the portion of the manufacturing device withinthe point of interest includes a component manufactured by themanufacturing device. In embodiments, the portion of the manufacturingdevice within the point of interest includes a bike chain manufacturedby the manufacturing device.

In embodiments, a system for detecting operating characteristics of amanufacturing device includes at least one data capture deviceconfigured to capture raw data of a point of interest of themanufacturing device, a memory, and a processor. The memory includesinstructions executable by the processor to: generate one or more imagedata sets using the raw data captured; identify one or more valuescorresponding to a portion of the manufacturing device within the pointof interest represented by the one or more image data sets; record theone or more values; compare the recorded one or more values tocorresponding predicted values; generate a variance data set based onthe comparison of the recorded on or more values and the correspondingpredicted values; identify an operating characteristic of themanufacturing device based on the variance data; and generate anindication indicating the operating characteristic.

In embodiments, the memory includes instructions further executable bythe processor to identify a corrective action responsive to identifyingthe operating characteristic. In embodiments, the memory includesinstructions further executable by the processor to initiate acorrective action responsive to identifying the operatingcharacteristics. In embodiments, the operating characteristic includes avibration of a component of the manufacturing device. In embodiments,the operating characteristic includes a shape of a component of themanufacturing device. In embodiments, the operating characteristicincludes a size of a component of the manufacturing device. Inembodiments, the operating characteristic includes a deflection of acomponent of the manufacturing device. In embodiments, the operatingcharacteristic includes an electromagnetic emission of a component ofthe manufacturing device. In embodiments, the operating characteristicincludes a temperature of a component of the manufacturing device. Inembodiments, the operating characteristic includes a temperature of agas within a component of the manufacturing device. In embodiments, theoperating characteristic includes a temperature of a liquid within acomponent of the manufacturing device. In embodiments, the operatingcharacteristic includes a temperature of a solid within a component ofthe manufacturing device. In embodiments, the operating characteristicincludes a pressure within a component of the manufacturing device. Inembodiments, the operating characteristic includes a pressure of a gaswithin a component of the manufacturing device. In embodiments, theoperating characteristic includes a pressure of a liquid within acomponent of the manufacturing device. In embodiments, the operatingcharacteristic includes a density of a gas within a component of themanufacturing device.

In embodiments, the operating characteristic includes a density of aliquid within a component of the manufacturing device. In embodiments,the operating characteristic includes a density of a solid within acomponent of the manufacturing device. In embodiments, the operatingcharacteristic includes a density of a component manufactured by themanufacturing device. In embodiments, the component includes a part fora vehicle. In embodiments, the component includes a part for a bike. Inembodiments, the component includes a bike chain. In embodiments, thecomponent includes a gasket. In embodiments, the component includes afastener. In embodiments, the component includes a part for a screw. Inembodiments, the component includes a part for a bolt. In embodiments,the component includes a part for a printed circuit board. Inembodiments, the component includes a part for a capacitor. Inembodiments, the component includes a part for a resistor. Inembodiments, the component includes a part for an inductor. Inembodiments, the operating characteristic includes a chemical structureof a gas within a component of the manufacturing device.

In embodiments, the operating characteristic includes a chemicalstructure of a liquid within a component of the manufacturing device. Inembodiments, the operating characteristic includes a chemical structureof a solid within a component of the manufacturing device. Inembodiments, the operating characteristic includes a chemical structureof a component manufactured by the manufacturing device. In embodiments,the component includes a part for a vehicle. In embodiments, thecomponent includes a part for a bike. In embodiments, the componentincludes a bike chain. In embodiments, the component includes a gasket.In embodiments, the component includes a fastener. In embodiments, thecomponent includes a part for a screw. In embodiments, the componentincludes a part for a bolt. In embodiments, the component includes apart for a printed circuit board. In embodiments, the component includesa part for a capacitor.

In embodiments, the component includes a part for a resistor. Inembodiments, the component includes a part for an inductor. Inembodiments, the data capture device includes an image capture device.In embodiments, the data capture device includes a camera. Inembodiments, the data capture device includes data measurement device.In embodiments, the data capture device includes a sensor. Inembodiments, the data capture device includes a full spectrum camera. Inembodiments, the data capture device includes radiation imaging device.In embodiments, the data capture device includes an X-ray imagingdevice. In embodiments, the data capture device includes a non-visiblelight data capture device. In embodiments, the data capture deviceincludes a visible light data capture device. In embodiments, the datacapture device includes sonic data capture device. In embodiments, thedata capture device includes an image capture device. In embodiments,the data capture device includes light imaging, detection, and rangingdevice. In embodiments, the data capture device includes point clouddata capture device. In embodiments, the data capture device includes aninfrared inspection device. In embodiments, the data capture deviceincludes an image capture device.

In embodiments, the data capture device includes a pressure sensor. Inembodiments, the data capture device includes a temperature sensor. Inembodiments, the data capture device includes a chemical sensor. Inembodiments, the data capture device includes a stand-alone device. Inembodiments, the data capture device includes associated with a mobiledevice. In embodiments, the mobile device includes a smart phone. Inembodiments, the mobile device includes a tablet. In embodiments, theraw data includes raw image data. In embodiments, the raw data includesraw measurement data. In embodiments, the portion of the manufacturingdevice within the point of interest includes a component of themanufacturing device. In embodiments, the portion of the manufacturingdevice within the point of interest includes a belt of the manufacturingdevice. In embodiments, the portion of the manufacturing device withinthe point of interest includes a component manufactured by themanufacturing device. In embodiments, the portion of the manufacturingdevice within the point of interest includes a bike chain manufacturedby the manufacturing device.

In embodiments, a computer vision system for detecting operatingcharacteristics of a manufacturing device, includes at least one datacapture device configured to capture raw data of a point of interest ofthe manufacturing device, a memory, and a processor. The memory includesinstructions executable by the processor to: generate one or more imagedata sets using the raw data captured; visually identify one or morevalues corresponding to a portion of the manufacturing device within thepoint of interest represented by the one or more image data sets; recordthe one or more values; visually compare the recorded one or more valuesto corresponding predicted values; generate a variance data set based onthe comparison of the recorded on or more values and the correspondingpredicted values; identify an operating characteristic of themanufacturing device based on the variance data; compare the operatingcharacteristic to a threshold; determine whether the operatingcharacteristic is within a tolerance based on whether the operatingcharacteristic is greater than the threshold; and generate an indicationindicating the operating characteristic.

In embodiments, the computer vision system is trained by a deep learningsystem. In embodiments, the deep learning system is configured to trainthe computer vision system using at least one training data set. Inembodiments, the at least one training data set includes image data. Inembodiments, the at least one training data set includes non-image data.

In embodiments, a computer vision system for detecting operatingcharacteristics of a device, includes at least one data capture deviceconfigured to capture raw data of a point of interest of the device, amemory and a processor. The memory includes instructions executable bythe processor to: generate one or more image data sets using the rawdata captured; visually identify one or more values corresponding to aportion of the device within the point of interest represented by theone or more image data sets; record the one or more values; visuallycompare the recorded one or more values to corresponding predictedvalues; generate a variance data set based on the comparison of therecorded one or more values and the corresponding predicted values;identify an operating characteristic of the device based on the variancedata; compare the operating characteristic to a threshold; determinewhether the operating characteristic is within a tolerance based onwhether the operating characteristic is greater than the threshold; andgenerate an indication indicating the operating characteristic.

In embodiments, the device includes an agitator. In embodiments, thedevice includes an airframe control surface vibration device. Inembodiments, the device includes a catalytic reactor. In embodiments,the device includes a compressor. In embodiments, the device includes aconveyor. In embodiments, the device includes a lifter. In embodiments,the device includes a pipeline. In embodiments, the device includes anelectric powertrain. In embodiments, the device includes a roboticassembly device. In embodiments, the device includes a device in a gasproduction environment. In embodiments, the device includes a device ina pharmaceutical environment.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs withremote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio with remotemonitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology with remotemonitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board with remotemonitoring.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs with remote monitoring isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information withremote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers with remotemonitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels with remotemonitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling with remotemonitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set with remote monitoring isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates with remote monitoring isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands with remotemonitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis with remotemonitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage with remotemonitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring with remote monitoring isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexerwith remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors withremote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system withremote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices with remote monitoring isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics with remote monitoring

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem in an industrial environment having an IoT distributed ledgerwith remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorwith remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network with remote monitoring isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector with remotemonitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform with remotemonitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data with remote monitoringis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms with remote monitoring isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream with remote monitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms with remotemonitoring is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messageswith remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events with remote monitoring isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicswith remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers with remote monitoring isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection withremote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data with remotemonitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path with remote monitoring isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path with remotemonitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path with remotemonitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path withremote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order withremote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingwith remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages with remote monitoring isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages withremote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics with remote monitoring isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions with remote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets withremote monitoring is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function with remote monitoring isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs withpredictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio with predictivemaintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features with predictive maintenance

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology with predictivemaintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board withpredictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs with predictive maintenanceis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information withpredictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers withpredictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels withpredictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements with predictive maintenance isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling withpredictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set with predictive maintenanceis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates with predictive maintenance isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands with predictivemaintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system with predictive maintenance isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis with predictivemaintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods with predictive maintenance isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage withpredictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring with predictive maintenance isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction with predictive maintenance isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexerwith predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors withpredictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system withpredictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices with predictive maintenance isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data with predictive maintenance isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics with predictivemaintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerwith predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorwith predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector with predictive maintenance isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network with predictive maintenance isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs with predictive maintenance isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector with predictivemaintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform withpredictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data with predictivemaintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms with predictive maintenanceis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine with predictive maintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream with predictive maintenance isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms with predictivemaintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messageswith predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events with predictive maintenanceis disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicswith predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers with predictive maintenance isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection withpredictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection with predictive maintenance isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data with predictivemaintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path with predictive maintenance isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path withpredictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path with predictivemaintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path withpredictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order withpredictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingwith predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages with predictive maintenanceis disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages withpredictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages with predictive maintenance

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics with predictivemaintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions with predictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets withpredictive maintenance is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function with predictivemaintenance is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs withpattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio with patternrecognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology with patternrecognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board with patternrecognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs with pattern recognition isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information withpattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers withpattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels with patternrecognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling withpattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set with pattern recognition isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates with pattern recognition isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands with patternrecognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis with patternrecognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage with patternrecognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring with pattern recognition isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction with pattern recognition isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexerwith pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors withpattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system withpattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices with pattern recognition isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data with pattern recognition isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics with pattern recognitionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerwith pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorwith pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem in an industrial environment having a remotely organizedcollector with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network with pattern recognition isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector with patternrecognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform with patternrecognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data with patternrecognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms with pattern recognition isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine with pattern recognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream with pattern recognition isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms with patternrecognition is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messageswith pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events with pattern recognition isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicswith pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers with pattern recognition isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection withpattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data with patternrecognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path with pattern recognition isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path with patternrecognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path with patternrecognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path withpattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order withpattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingwith pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages with pattern recognition isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages withpattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics with pattern recognitionis disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions with pattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets withpattern recognition is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function with pattern recognitionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio for manufacturing isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs for manufacturing isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set for manufacturing isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands for manufacturingis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexer formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics for manufacturing isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerfor manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorfor manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector for manufacturing isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data for manufacturing isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms for manufacturing isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream for manufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms formanufacturing is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messagesfor manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events for manufacturing isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicsfor manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection formanufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data for manufacturing isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path formanufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path formanufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path formanufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order formanufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingfor manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages for manufacturing isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages formanufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics for manufacturing isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions for manufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets formanufacturing is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function for manufacturing isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs forfossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio for fossil fuelenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features for fossil fuel energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology for fossil fuelenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board for fossilfuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs for fossil fuel energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information forfossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers for fossilfuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels for fossilfuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements for fossil fuel energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling for fossilfuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set for fossil fuel energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates for fossil fuel energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands for fossil fuelenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system for fossil fuel energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition for fossil fuel energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis for fossil fuelenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods for fossil fuel energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage for fossilfuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring for fossil fuel energy productionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction for fossil fuel energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexer forfossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors forfossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system forfossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices for fossil fuel energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data for fossil fuel energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics for fossil fuel energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback for fossil fuel energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors for fossil fuel energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerfor fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorfor fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector for fossil fuel energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network for fossil fuel energy productionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs for fossil fuel energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR for fossil fuel energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector for fossil fuelenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform for fossilfuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data for fossil fuel energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms for fossil fuel energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine for fossil fuel energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream for fossil fuel energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms for fossil fuelenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions for fossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits for fossil fuel energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size for fossil fuel energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messagesfor fossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events for fossil fuel energyproduction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicsfor fossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers for fossil fuel energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers for fossil fuel energy productionis disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery for fossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection forfossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection for fossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection for fossil fuel energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection for fossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection for fossil fuel energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection for fossil fuel energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection for fossil fuel energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data for fossil fuelenergy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path for fossil fuel energyproduction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path for fossilfuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path for fossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path for fossil fuelenergy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path forfossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order forfossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingfor fossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages for fossil fuel energyproduction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages forfossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages for fossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections for fossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics for fossil fuel energyproduction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) for fossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions for fossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets forfossil fuel energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function for fossil fuel energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs foraerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio for aerospace isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology for aerospace isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board foraerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information foraerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers foraerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels for aerospaceis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling foraerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands for aerospace isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis for aerospace isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage foraerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexer foraerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors foraerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system foraerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics for aerospace isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerfor aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorfor aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector for aerospace isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform foraerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data for aerospace isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream for aerospace is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms for aerospace isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messagesfor aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicsfor aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection foraerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data for aerospace isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path foraerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path for aerospace isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path foraerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order foraerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingfor aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages foraerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics for aerospace isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions for aerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets foraerospace is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function for aerospace isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs formining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio for mining isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology for mining isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board for miningis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information formining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers for miningis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels for mining isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling for miningis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands for mining isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis for mining isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage for miningis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexer formining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors formining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system formining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerfor mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorfor mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector for mining isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform for miningis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data for mining isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms for mining isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messagesfor mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicsfor mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection formining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data for mining isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path for miningis disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path for mining isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path formining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order formining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingfor mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages formining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions for mining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets formining is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function for mining is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs forconstruction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio for construction isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology for constructionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board forconstruction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs for construction isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information forconstruction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers forconstruction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels forconstruction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling forconstruction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set for construction isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands for constructionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis for constructionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage forconstruction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexer forconstruction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors forconstruction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system forconstruction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics for construction isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerfor construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorfor construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector for construction isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform forconstruction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data for construction isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms for construction isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream for construction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms for constructionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messagesfor construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events for construction isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicsfor construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection forconstruction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data for construction isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path forconstruction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path for constructionis disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path forconstruction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order forconstruction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingfor construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages for construction isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages forconstruction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics for construction isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions for construction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets forconstruction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function for construction isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs forships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio for ships isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology for ships isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board for ships isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information forships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers for shipsis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels for ships isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling for shipsis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands for ships isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis for ships isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage for ships isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexer forships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors forships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system for shipsis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerfor ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorfor ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector for ships isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform for ships isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data for ships isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms for ships isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messagesfor ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicsfor ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection forships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data for ships isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path for ships isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path for ships isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path forships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order forships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingfor ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages forships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions for ships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets forships is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function for ships is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs forsubmarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio for submarine isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology for submarine isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board forsubmarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information forsubmarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers forsubmarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels for submarineis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling forsubmarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands for submarine isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis for submarine isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage forsubmarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexer forsubmarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors forsubmarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system forsubmarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics for submarine isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerfor submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorfor submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector for submarine isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform forsubmarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data for submarine isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream for submarine is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms for submarine isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messagesfor submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicsfor submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection forsubmarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data for submarine isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path forsubmarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path for submarine isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path forsubmarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order forsubmarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingfor submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages forsubmarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics for submarine isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions for submarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets forsubmarine is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function for submarine isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs forwind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio for wind energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology for wind energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board for windenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs for wind energy productionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information forwind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers for windenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels for windenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling for windenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set for wind energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates for wind energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands for wind energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis for wind energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage for windenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring for wind energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction for wind energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexer forwind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors forwind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system for windenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices for wind energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data for wind energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics for wind energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerfor wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorfor wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector for wind energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network for wind energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector for wind energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform for windenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data for wind energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms for wind energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine for wind energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream for wind energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms for wind energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messagesfor wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events for wind energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicsfor wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers for wind energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection forwind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection for wind energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data for wind energyproduction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path for wind energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path for windenergy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path for wind energyproduction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path forwind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order forwind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingfor wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages for wind energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages forwind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics for wind energy productionis disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions for wind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets forwind energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function for wind energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio for hydroelectricenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features for hydroelectric energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs for hydroelectric energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements for hydroelectric energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates for hydroelectric energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set for hydroelectric energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates for hydroelectric energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands for hydroelectricenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis for hydroelectric energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system for hydroelectric energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition for hydroelectric energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods for hydroelectric energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring for hydroelectric energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction for hydroelectric energy productionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation for hydroelectric energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexer forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices for hydroelectric energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data for hydroelectric energy productionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics for hydroelectric energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback for hydroelectric energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors for hydroelectric energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerfor hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorfor hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector for hydroelectric energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network for hydroelectric energy productionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs for hydroelectric energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR for hydroelectric energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector for hydroelectricenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data for hydroelectricenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms for hydroelectric energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine for hydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream for hydroelectric energy productionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms forhydroelectric energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions for hydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits for hydroelectric energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size for hydroelectric energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messagesfor hydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events for hydroelectric energyproduction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicsfor hydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers for hydroelectric energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers for hydroelectric energyproduction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery for hydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection forhydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection for hydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection for hydroelectric energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection for hydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection for hydroelectric energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection for hydroelectric energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection for hydroelectric energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data for hydroelectricenergy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path for hydroelectric energyproduction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path forhydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path for hydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path forhydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path forhydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order forhydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingfor hydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages for hydroelectric energyproduction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages forhydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages for hydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections for hydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics for hydroelectric energyproduction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) for hydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions for hydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets forhydroelectric energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function for hydroelectric energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs fornuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio for nuclear energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology for nuclearenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board for nuclearenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs for nuclear energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information fornuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers for nuclearenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels for nuclearenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements for nuclear energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling for nuclearenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set for nuclear energy productionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates for nuclear energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands for nuclear energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system for nuclear energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis for nuclearenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods for nuclear energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage for nuclearenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring for nuclear energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction for nuclear energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexer fornuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors fornuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system fornuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices for nuclear energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data for nuclear energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics for nuclear energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerfor nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorfor nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector for nuclear energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network for nuclear energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs for nuclear energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector for nuclear energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform for nuclearenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data for nuclear energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms for nuclear energy productionis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine for nuclear energy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream for nuclear energy production isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms for nuclearenergy production is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions for nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits for nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size for nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messagesfor nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events for nuclear energy productionis disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicsfor nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers for nuclear energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers for nuclear energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery for nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection fornuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection for nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection for nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection for nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection for nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection for nuclear energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection for nuclear energy production isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data for nuclear energyproduction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path for nuclear energy productionis disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path for nuclearenergy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path for nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path for nuclearenergy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path fornuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order fornuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingfor nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages for nuclear energy productionis disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages fornuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages for nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections for nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics for nuclear energyproduction is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) for nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions for nuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets fornuclear energy production is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function for nuclear energyproduction is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs foroil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio for oil drilling isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology for oil drillingis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board for oildrilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs for oil drilling isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information foroil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers for oildrilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels for oildrilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling for oildrilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set for oil drilling isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands for oil drillingis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis for oil drillingis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage for oildrilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexer foroil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors for oildrilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system for oildrilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics for oil drilling isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerfor oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorfor oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector for oil drilling isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform for oildrilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data for oil drilling isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms for oil drilling isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream for oil drilling is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms for oil drillingis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messagesfor oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events for oil drilling isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicsfor oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection foroil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data for oil drilling isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path for oildrilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path for oil drillingis disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path for oildrilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order foroil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingfor oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages for oil drilling isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages foroil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics for oil drilling isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions for oil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets foroil drilling is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function for oil drilling isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of an analog crosspoint switch for collecting variable groups of analog sensor inputs foroil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having up front signal conditioningon a multiplexer for improved signal-to-noise ratio for oil pipelines isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having multiplexer continuousmonitoring alarming features for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having high-amperage inputcapability using solid state relays and design topology for oilpipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having power-down ability of atleast one of an analog sensor channel and a component board for oilpipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having unique electrostaticprotection for trigger and vibration inputs for oil pipelines isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having precise voltage referencefor A/D zero reference for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a phase-lock loop band-passtracking filter for obtaining slow-speed RPMs and phase information foroil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having digital derivation of phaserelative to input and trigger channels using on-board timers for oilpipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having routing of a trigger channelthat is either raw or buffered into other analog channels for oilpipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling for oilpipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having long blocks of data at ahigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having storage of calibration datawith maintenance history on-board card set for oil pipelines isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a rapid route creationcapability using hierarchical templates for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having intelligent management ofdata collection bands for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a neural net expert systemusing intelligent management of data collection bands for oil pipelinesis disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having use of a database hierarchyin sensor data analysis for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a graphical approach forback-calculation definition for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having proposed bearing analysismethods for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having torsional vibrationdetection/analysis utilizing transitory signal analysis for oilpipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having improved integration usingboth analog and digital methods for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having data acquisition parkingfeatures for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-sufficient dataacquisition box for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having SD card storage for oilpipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having extended onboard statisticalcapabilities for continuous monitoring for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of ambient, localand vibration noise for prediction for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart route changes routebased on incoming data or alarms enable simultaneous dynamic data foranalysis or correlation for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having smart ODS and transferfunctions for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having hierarchical multiplexer foroil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having identification sensoroverload for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having RF identification and aninclinometer for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having continuous ultrasonicmonitoring for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternrecognition based on fusion of remote, analog industrial sensors for oilpipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system for oilpipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having cloud-based policyautomation engine for IoT, with creation, deployment and management ofIoT devices for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having on-device sensor fusion anddata storage for industrial IoT devices for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing datamarketplace for industrial IoT data for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having self-organization of datapools based on utilization and/or yield metrics for oil pipelines isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having training AI models based onindustry-specific feedback for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organized swarm ofindustrial data collectors for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT distributed ledgerfor oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing collectorfor oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a network-sensitivecollector for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a remotely organizedcollector for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing storagefor a multi-sensor data collector for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a self-organizing networkcoding for multi-sensor data network for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical and/or sound outputs for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having heat maps displayingcollection data for AR/VR for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having automatically tuned AR/VRvisualization of data collected by a data collector for oil pipelines isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT platform for oilpipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter forreceiving data inputs and establishing a connection with one or moreavailable IoT cloud platforms to publish the data for oil pipelines isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a condition detector fordetecting conditions related to connect attempts made by the IoT dataadapter to one or more IoT cloud platforms for oil pipelines isdisclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having an IoT data adapter with anadaptation engine for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having the use of machine learningto prepare a data packet or stream for oil pipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a data marketplace thatprovides a pool of available cloud networking platforms for oilpipelines is disclosed.

In embodiments, a system for data collection, using a computer visionsystem, in an industrial environment having a messaging utility thatprovides a cloud platform user interface with a message indicating theavailability of a new data source and data source integration and usageinstructions for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain a first and secondtransmission limit based on received rate of arrival and success ofdelivery feedback messages, and limiting transmission of messages basedon the transmission limits for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to limit transmission offurther messages not yet acknowledged as successfully deliveredaccording to the window size for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain an estimate of arate of loss events and use it to adjust the rate of redundancy messagesfor oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having an estimated rate of loss events where theerror correction code used to determine redundancy messages chosen isbased on the estimated rate of loss events for oil pipelines isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to apply forward errorcorrection based on messages received describing channel characteristicsfor oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying transmission offeedback messages using timers for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events and delaying modification ofcongestion window size based on timers for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to maintain/set timers based onoccurrences of delivery order events, delaying modification ofcongestion window size based on timers, and cancelling modification ofcongestion window size when receiving a feedback message indicatingsuccessful delivery for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a current/previous connection foroil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing an error rate of a current/previousconnection for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing a timing variability of acurrent/previous connection for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing bandwidth of a current/previousconnection for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing round trip time of acurrent/previous connection for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure a new connectionusing maintained data characterizing communication control parameters ofa current/previous connection for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to configure new connectionusing maintained data characterizing forward error correction parametersof a current/previous connection for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a data store for maintaining datacharacterizing one or more current or previous data communicationconnections and a connection initiation module for initiating new datacommunication connections based on maintained data for oil pipelines isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages over a lower latency data path and a second subset ofmessages over a higher latency data path for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofdata messages that are time critical over a lower latency data path anda second subset of messages over a higher latency data path for oilpipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first initialsubset of data messages over a lower latency data path and a secondsubset of messages that are subsequently available over a higher latencydata path for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofacknowledgment messages over a lower latency data path and a secondsubset of data messages over a higher latency data path for oilpipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to transmit a first subset ofsupplemental/redundancy data messages over a lower latency data path anda second subset of data messages over a higher latency data path for oilpipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order foroil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that increases as the position of the messages is non-decreasingfor oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages for oil pipelines isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a degree of redundancy associated with eachmessage that is based on message position in the transmission order andin response to receiving feedback messages, and adding or removingredundancy messages from the queue based on the feedback messages foroil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to adjust the number ofmessages sent over each of multiple different data paths with differentcommunication protocols if it is determined that a data path is alteringflow of messages initial division based on previous communicationconnections for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to modify/add/remove redundancyinformation associated with encoded data as it travels from node to nodevia channels based on channel characteristics for oil pipelines isdisclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having the ability to send FEC packets at anestimated rate of loss events (isolated packet loss or burst ofconsecutive packets) for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having combined coding, TCP, and pacing of packettransmissions for oil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a forward error correction codeconstruction that interleaves groups of message packets and paritypackets and has coding across overlapping groups of message packets foroil pipelines is disclosed.

In embodiments, a system for data communication, using a computer visionsystem, between nodes having a variant of TCP that combines delay-basedbackoff with a stable window increase function for oil pipelines isdisclosed.

In embodiments, flow of information among participants and elements of apredictive maintenance knowledge platform may be configured as depictedin FIG. 302. A platform 28600 as exemplary configured in FIG. 302 mayinclude a plurality of subsystems that may include one or more of: datastorage, machine intelligence, and industrial machine-relatedtransactions. Such a subsystem may be a web-server based system, adistributed system, a handheld device, an industrial machine co-residentsystem, and the like. In an example, the industrial machine maintenancedata analysis subsystem 28602 may include a data storage 28604, machinelearning and/or an artificial intelligence facilities 28606, atransaction facility 28608 and the like. The Industrial machinemaintenance data analysis subsystem 28602 may provide services 28610including updates to industrial machine related data, such as servicecriteria, fault prevention, service pricing, parts pricing, tests andcriteria for detecting potential machine faults, analysis of repairs andthe like, functions and updates to fault prediction metadata, and thelike. The industrial machine maintenance data analysis subsystem 28602may provide information, such as those associated with the providedservices 28610, in the form of streams, transactions, data base readingand writing, and the like for access to cloud-based data storage. Theindustrial machine maintenance data analysis subsystem 28602 may receiveinformation regarding individual industrial machines from the machinesvia the data collection network 28612. In embodiments, a data collectionnetwork 28612 may be described herein and in the documents referencedand incorporated herein. The industrial machine maintenance dataanalysis subsystem 28602 may receive information from specificindustrial machines such as machine parameters and the like that may beretrieved from one or more smart RFID elements 28614 of the industrialmachine. In embodiments, smart RFID elements may be configured withportions of industrial machine and may have functionality as describedelsewhere herein.

In embodiments, an industrial machine predictive maintenance subsystem28616 may apply machinery fault detection, identification,classification, and related algorithms to the data provided from theindustrial machine maintenance data analysis subsystem 28602 and to datafurther provided from an industrial machine health monitoring facilities28618 and the like to generate data structures, streams, and otherelectronic data that may be communicated to facilitate predictivemaintenance of industrial machines. In embodiments, the industrialmachine predictive maintenance subsystem 28616 may receive and analyze astream or the like of industrial health monitoring data from theindustrial machine health monitoring facility 28618. One or more resultsof such stream analysis may include determination of conditions thatindicate a healthy machine, an unhealthy machine, a likelihood of atleast a portion of a machine that may need service to avoid a fault, aspecific machine that requires service, and the like. Conditions thatmay indicate a healthy machine may be a result of tests and the likeperformed on or by industrial machines and communicated to the machinehealth monitoring facility 28618. In an example, the machine healthmonitoring facility 28618 may receive operation-related information,such as sensor data from industrial machine motors (e.g., torque,revolutions per minute, run time, start/stop data, directional data andthe like) in a live or delayed stream from one or more industrialmachines. This operation-related data may be processed by the healthmonitoring facility 28618 to detect when, for example, a number ofrevolutions over a set period of time, such as a day, week, month andthe like exceeds a maintenance threshold value. A portion of the streamdata and/or the result of processing by the health monitoring facility28618 may be provided, such as a stream and the like to the industrialmachine predictive maintenance subsystem 28616 for uses as described,including identifying potential faults and the like that are to beaddressed with predictive maintenance and the like. The industrialmachine predictive maintenance subsystem 28616 may generate one or morepredictive maintenance sets of data 28620 that may identify one or moreindustrial machines and may indicate portion(s) of the machine that aredetermined to benefit from service, maintenance, repair, replacement andthe like. The sets of data 28620 may include specific parts, serviceprocedures, materials, service timeframes, required to perform apredictive maintenance activity on one or more specific industrialmachines. In embodiments, machine fault analysis that may be performedby the industrial machine predictive maintenance subsystem 28616 mayfacilitate generating work orders from a CMMS subsystem 28622.

In embodiments, the CMMS subsystem 28622 may receive industrial machinedetails, service (e.g., repair, maintenance, upgrade, and the like)details for the industrial machine, procedures to be followed, partsneeded, and the like from sources such as the industrial machinepredictive maintenance subsystem 28616, a CMMS interface 28624, datastructures configured and maintained that may include parts lists andthe like for the industrial machine and any other information tofacilitate performing service on the industrial machine. The CMMSsubsystem 28622 may initiate actions with parts suppliers, serviceproviders, third-party partners, vendors, an owner/operator of theindustrial machine to be serviced and the like. In an example, the CMMSsubsystem 28622 may generate orders for services from one or moreservice providers that are known to the CMMS subsystem 28622 asqualified to provide the services required.

In embodiments, the CMMS subsystem 28622 may interface with one or morepredictive maintenance knowledge bases and/or knowledge graphs that maybe stored in a data store accessible by the CMMS subsystem. Inembodiments, such a CMMS knowledge base or the like may further includea knowledge graph that may contain information beneficial to the servicedetermination and order generation services provided by the CMMSsubsystem 28622. A CMMS knowledge graph may contain or provide computeraccess to information about industrial machines, service activity ofindustrial machines, costs (e.g., historical, trending, and predictive)for parts, materials, tools, and services of industrial machines,algorithms and functionality for delivering the CMMS services 28626 andthe like. The CMMS subsystem 28622 may facilitate coordination withservice providers, parts providers, material and tool providers and thelike based on an industrial machine owner's decision regarding servicingthe industrial machine so that the service can be performed in atimeframe that the owner chooses.

The CMMS subsystem 28622 may access information in the smart RFIDelement(s) 28614 via the CMMS interface 28624 that may facilitate accessto individual industrial machines and the like. The CMMS subsystem 28622may use information received via the CMMS interface 28624 to facilitateperforming coordination of resources to perform maintenance effectivelyand efficiently for the specific machine. In an example, a specificindustrial machine may have an operating cycle that results in greaterutilization of one of its moving parts (e.g., an industrial motor) thantypical. This information may be processed by the predictive maintenancesubsystem 28616 and result in an indication of a service that may needto be performed on the machine. The predictive maintenance subsystem28616 may provide information to the CMMS subsystem 28622 that it wouldprocess to generate orders for parts, services, and the like. Thisknowledge may be used by the CMMS subsystem 28622 to interact withservice, parts, and material suppliers to provide a firm quote forperforming a utilization-based maintenance service at a different time(e.g., weeks or months sooner) than other comparable industrial machineswith lower utilization rates.

In embodiments, the CMMS subsystem 28622 may execute algorithms thatgather information about a plurality of industrial machines, including aplurality of industrial machines of different types of machine (e.g.,stationary machines, mobile machines, machines on vehicles, machinesdeployed at job sites, and the like) along with service providerinformation, parts and parts provider information, part location andinventory information, machine production providers, third-party partshandlers, logistics providers, transportation providers, servicestandards, service requirements, service activities including results ofservice and the like, and other information to facilitate providingservices 28626 including coordinating orders for services, parts and thelike.

In embodiments, in response to industrial machine fault identificationinformation provided from the preventive maintenance subsystem 28616,the predictive maintenance knowledge system 30002 may identify candidateservice providers. Service providers that are known to the CMMSsubsystem 28622 as having successfully demonstrated experience with theprocedure needed for the requested service may be contacted to provide aservice estimate and/or a price estimate for service, parts, and thelike. Similarly, parts and/or material that may be associated with theprocedure of the requested service may be identified. Factors such aspart cost, transportation costs, availability, location of the partsversus the machines, prior relationships between one or more partsproviders and a party associated with the service request, such as theindustrial machine owner and the like, and other factors may beevaluated to determine which parts provider to contact in preparationfor ordering the parts. With these factors considered, a part inquirymay be placed with one or more parts providers in anticipation of theservice being conducted by the qualified service indication from thepreventive maintenance subsystem 28616 with one or more servicerecommendations. In embodiments, the CMMS subsystem 28622 may haveenough information to automatically select a specific servicerecommendation and may, with or without explicit approval, generate aservice order 28626 that may include a parts/material/tools order ifneeded for the requested service.

In embodiments, information that the CMMS subsystem 28622 may rely onmay be sourced from an Enterprise Resource Planning (ERP) interfaceassociated with the industrial machine as well as third-party sources ofinformation such as independent parts suppliers, service providers, andthe like that may offer parts and/or services for industrial machines.In embodiments, the CMMS subsystem 28622 may coordinate with anindustrial machine owner's ERP system, such as via the ERP interface28628 to effect placement of orders with the service provider, partsprovider, and the like. The CMMS subsystem 28622 may use servicematerial provider information to determine price and availability ofservice material. This information may be combined with service materialinventory information to facilitate generating suitable orders forservice material as part of the industrial machine service offering28626.

In embodiments, the CMMS subsystem 28622 may receive a timeframe inwhich the repair must be completed in order to avoid failure and therecommended repair with instructions from the manufacturers manual onhow to conduct the repair. This repair information may be then processedby the CMMS subsystem 28622 (e.g., a cloud based system) where a workorder is created and tracked. The work order may be digitally pushed tothe ERP system to check the plant's production schedule to find when thespecific machine requiring maintenance is available for repair based onthe time frame provided by the analysis and the amount of time themachine will be off-line based on, for example information in amanufacturer's manual referenced in a service procedure that states howmuch time it should take to make the repair. Once the ERP system findsthe available date it may coordinate with the CMMS subsystem 28622 toask for bids from vendors for the parts and the service work or to placeorders for the parts and with a service contractor, such as a preferredcontractor. In embodiments, the CMMS subsystem 28622 or the ERP systemmay configure a request for bids by simply using the manufacturersmanual for the procedure to provide the bidders with the required partsinformation (e.g., part numbers, vintage, revision, specifications,after-market alternatives, last price paid, if a used part is OK, andthe like) and the repair actions necessary for the service action (e.g.,the procedure steps, diagnostics, equipment/tools required, materialsrequired, personnel required, and the like). A bid may be based on therepair actions listed in the procedure and may become the scope of workfor the job to be bid. In embodiments, if there are other problems foundand addressed outside of this scope a secondary process may be followedto approve additional compensation to the vendor.

In embodiments, a service delivery and tracking subsystem 28630 may beused by service providers, such as service technicians, industrialmachine owners/operators, third parties (e.g., auditors, regulators,union personnel, safety associations, parts manufacturers and the like)to gather and report information associated with an ordered servicerequest as may be determined from service order data 28626 The servicedelivery and tracking subsystem 28630 may include functionality thatmatches up machine procedures with service requirements, ensures thatimages associated with the ordered service (e.g., a part being services,an installation of the machine, a video of the machine operating beforeand/or after service, parts that have been removed from the industrialmachine, service personnel, and the like) are captured with sufficientquality to meet image quality standards for automatic detection of oneor more parts of the industrial machine.

In embodiments, the service delivery and tracking subsystem 28630 mayreport data, repairs, images and the like, collectively service data28632 to an industrial machine maintenance data analysis subsystem 28602for refinement of service procedures, parts ordering, and the like.

In embodiments, compensation for work and analysis performed by thevarious subsystems may be derived from various sources. The CMMSsubsystem 28622 operator/owner/affiliate may be compensated on atransaction basis, such as by receiving a fee for each part or serviceordered. Such a fee may include a fixed portion (e.g., amount per partorder) and may include a variable portion (e.g., a percent of an ordertotal). This fee may be explicitly included in charges billed to a partyresponsible for payment of the parts and services to perform themaintenance action. This fee may be built into the cost of eachpart/service and recovered as a deduction from the payment that ispassed from the responsible party to the parts and/or service provider.

In embodiments, an industrial machine predictive maintenance system mayinclude an industrial machine data analysis facility that generatesstreams of industrial machine health monitoring data by applying machinelearning to data representative of conditions of portions of industrialmachines received via a data collection network. The system may furtherinclude an industrial machine predictive maintenance facility thatproduces industrial machine service recommendations responsive to thehealth monitoring data by applying machine fault detection andclassification algorithms thereto. The system may further include acomputerized maintenance management system (CMMS) that produces at leastone of orders and requests for service and parts responsive to receivingthe industrial machine service recommendations. And, the system mayinclude a service and delivery coordination facility that receives andprocesses information regarding services performed on industrialmachines responsive to the at least one of orders and requests forservice and parts, thereby validating the services performed whileproducing a ledger of service activity and results for individualindustrial machines.

In embodiments, methods and systems for finding a set of workers havingrelevant know-how and expertise about maintenance, service and repair ofa specific machine may employ machine learning algorithms with workerselection algorithms to ensure timely, quality workers are selected anddeployed for industrial machine servicing, such as for predictivemaintenance and the like described herein. Referring to FIG. 303,machine learning-based methods for finding a set of workers as describedabove is depicted. In embodiments, the facility for finding workers28702 may be configured as a system that may include a set of algorithmsand data structures that may execute on a processor. The worker findingfacility 28702 may process data about workers, machines, procedures, andthe like with algorithms that facilitate matching qualified workers withservice activities, such as predictive maintenance activities and thelike. In an example of finding workers, a service activity may includefollowing a service or maintenance procedure 28706, such as to repairand/or maintain a portion of an industrial machine. The procedure 28706may further indicate one or more industrial machines, such as by modelnumber, family, and the like. The worker finding facility 28702 mayfurther access, such as by retrieving information about workers from aworker database 28722, information that facilitates characterizing oneor more workers, including procedures for which the worker hasexperience, training, certification and the like. One or more workerswho have experience and the like with the procedure may be selected forfurther refinement, which may include matching a worker location to amachine location, a worker availability and/or schedule to a machineservice schedule, worker rates/fees to machine owner service budgets andthe like. One or more workers on a resulting list of refined workers maybe contacted about a service to be performed on the machine. Based on,for example, replies to such worker contact, a primary worker may beselected by the worker finding facility 28702 and allocated to performthe service via the procedure 28706.

In embodiments, the worker finding facility 28702 may access a list ofprocedures 28706 for which service may be required. The worker findingfacility 28702 may build a data set of workers that qualify forperforming the procedure, such as by searching through workerinformation 28716 for workers who meet procedure criteria, such as anumber of times the worker has performed the procedure, a number oftimes a worker has performed a similar procedure, and the like. Workerswith more experience may be marked as preferred workers in such adatabase for the specific procedure so that when the procedure isrequired to be performed, those preferred workers may be readilyidentified. In embodiments, workers may directly maintain the workerdatabase 28722 by updating information regarding procedures and the likethat they perform.

In embodiments, the worker finding facility 28702 may receiveinformation about procedures 28706, machines 28708, machine location28710, machine owner and/or affiliation 28712, required service schedule28714 and the like for one or more service activities, such as apredictive maintenance activity and the like to be performed and form aprofile of a preferred worker for a given combination of procedure,machine, location, owner, schedule and the like. The worker findingfacility 28702 may build a profile for various combinations of suchinformation so that workers that best meet the profile may be readilyfound. In embodiments, such preferred worker profiles may be publishedso that third parties, such as service organizations and the like mayprovide estimates and the like for providing a service based on theprofile. These estimates may be captured and used by the methods andsystems of predictive maintenance of industrial machines and the like tobuild a marketplace of service providers for common or often requiredservices, such as preventive maintenance services and the like.

In embodiments, information captured in the worker database 28722 andthe like may be processed with machine learning algorithms 28724 tofacilitate improving matching of workers with requirements for providingqualified workers for procedures and the like. In embodiments, thepreferred worker profiles and information received in response to theirpublication may be processed with the machine learning algorithms 28724to refine the algorithms that are used to build preferred workerprofiles.

In embodiments, additional information that may influence workerselection by the worker finding facility 28702 may include affiliationof the worker with service organizations, manufacturers of industrialmachines, industry organizations, and the like. Referrals and orfeedback on specific workers may be factored into determination ofindividual workers, worker groups and the like as to their preferredworker status and the like. Worker rates and/or fees (e.g., based onestimates, actual charges, payment terms and the like) may further befactored into finding a worker, such that workers that when two or moreworkers overall have comparable qualifications, a worker with lowercosts or easier payment terms may be ranked higher for a given procedurethan one with higher cost and the like.

In embodiments, techniques for finding workers may be performed inreal-time or near real time as demands for industrial machines require.In this way, as new workers become available, finding a worker mayincorporate updates to worker profiles and the like that may beaccessible over websites, and the like via the Internet.

In embodiments, a system may include an industrial machine predictivemaintenance facility that produces industrial machine servicerecommendations by applying machine fault detection and classificationalgorithms to industrial machine health monitoring data. Such a systemmay also include a worker finding facility that identifies at least onecandidate worker for performing a service indicated by the industrialmachine service recommendations by correlating information in therecommendation regarding at least one service to be performed with atleast one of experience and know-how for industrial service workers inan industrial service worker database. In embodiments, the system mayinclude machine learning algorithms executing on a processor thatimprove the correlating based on service-related information for aplurality of services performed on similar industrial machines andworker-related information for a plurality of services performed by theat least one candidate worker.

In embodiments, an industrial machine maintenance part/service orderingfacility 28802 for industrial machine service and maintenance, includingpredictive maintenance and the like may be embodied as depicted at leastin FIG. 304 filed herewith. The industrial machine maintenancepart/service ordering facility 28802 may facilitate finding, ordering,and fulfilling orders for relevant parts and components, so thatmaintenance, service and repair operations for industrial machines canoccur seamlessly, with minimal disruption. In embodiments, theindustrial machine maintenance part/service ordering facility 28802 mayreceive industrial machine details 28808, service (e.g., repair,maintenance, upgrade, and the like) details 28810 for an industrialmachine, procedures to be followed 28806, parts needed 28814, serviceproviders 28820, parts providers 28822 and the like. The industrialmachine maintenance part/service ordering facility 28802 may initiateactions with parts suppliers, service providers, third-party partners,vendors, an owner/operator of the industrial machine to be serviced andthe like. In an example, the industrial machine maintenance part/serviceordering facility 28802 may generate orders for services 28818 from oneor more service providers 28820 that are known to the industrial machinemaintenance part/service ordering facility 28802 as qualified to providethe services required. The industrial machine maintenance part/serviceordering facility 28802 may also generate orders for parts 28816 fromone or more parts providers 28822 that are known as qualified to providethe parts required, on time, within budget, and the like. The partsorders 28816 and the service orders 28818 may also be communicated to anowner 28812 or other entity responsible for ensuring access to theindustrial machine. The parts and service providers selected may furthercoordinate with the owner 28812 to ensure the service can be properlydelivered. The industrial machine maintenance part/service orderingfacility 28802 may have access to the machine owner 28812 preferencesand/or requirements regarding scheduling, budgets, service and partsprovider preferences and/or affiliations, and the like to facilitatecoordination with service providers, parts providers, material and toolproviders and the like based thereon.

Factors such as part cost, transportation costs, availability, locationof the parts versus the machines, prior relationships between one ormore parts providers and a party associated with the service request,such as the industrial machine owner and the like, and other factors maybe evaluated to determine which parts provider 28822 to contact inpreparation for ordering the parts 28816. With these factors considered,a part inquiry may be placed with one or more parts providers 28822 inanticipation of the service being conducted by the qualified serviceprovider. In embodiments, the industrial machine maintenanceparts/service ordering facility 28802 may have enough information toautomatically select a specific service provider 28820 and may, with orwithout explicit approval, generate the service order 28818.

In embodiments, information that the industrial machine maintenancepart/service ordering facility 28802 may rely information regardingvendors, and the like from an Enterprise Resource Planning (ERP) systemowned and or operated by the owner of the industrial machine. Inembodiments, the industrial machine maintenance part/service orderingfacility 28802 may coordinate with an industrial machine owner's ERPsystem to effect placement of orders with the service provider, partsprovider, and the like.

In embodiments, a system may include an industrial machine maintenancepart and service ordering facility that prepares and controls orders forparts and services responsive to service recommendations received froman industrial machine predictive maintenance facility that producesindustrial machine service recommendations by applying machine faultdetection and classification algorithms to industrial machine healthmonitoring data. In embodiments, the system may further analyze aprocedure associated with the service recommendations for generating atleast one of the orders for parts and services.

In embodiments, an industrial machine predictive maintenance system mayinclude deployment of smart RFID devices on portions of industrialmachines. The smart RFID devices may be configured to includeinformation about the machine, such as configuration information,assembly information, physical element details (e.g., part numbers,revisions, production details, test details, and the like), procedureinformation (e.g., assembly, disassembly, test, configuration, service,parts replacement, and the like), and other operational information andthe like. Smart RFID devices may be disposed with each major element ina machine, such as each element that might include information relevantfor efficient service and maintenance of the machine. In embodiments,disposing smart RFID devices may be configured into the production ofindustrial machine and the like parts and sub systems so that productioninformation and the like of the part(s) can be captured for the specificpart, and the like. A smart RFID element may not only provide storagefor a range of information, including large service manuals and thelike, a smart RFID element may include functionality, such as searching,indexing, linking, and the like that may facilitate users quicklyfinding procedures, such as lubricating procedures, bearing replacementprocedures, bearing fault frequencies, and the like that may be crucialfor machine trouble shooting and the like. In embodiments, at least onemethod for accessing the information may be compatible with existingtechniques used by expert service personnel, which may be taught to newservice providers while these experts remain on the job. In embodiments,providing easy access, including indexing, linking and the like may bebuilt into the documents, procedures, data sheets, manuals and the likeduring their creation so that common access approaches can be used forany embodiment of the information (e.g., in the smart RFID, in a cloudrepresentation of the RFID, in 3^(rd) party service manuals, inindustrial machine producer systems and the like).

Referring to FIG. 305, an industrial machine 28900 may be configuredfrom a plurality of elements, parts, sub-assemblies and the like. Onesuch sub-assembly might include an industrial machine motor 28902. AnRFID device may be disposed with the machine that may include details,such as those described herein for smart RFID devices, for the specificmotor. The motor 28902 RFID device may communicate, such as throughwireless communication with other devices brought into proximity, suchas a smart phone, tablet or the like 28914 so that a user of the tableand the like 28914 may access the information stored on the motor 28902RFID device for conducting service, maintenance, testing, and the like.In embodiments, the motor 28902 service procedure may be retrieved fromthe motor 28902 RFID and displayed via an application executing on thetable 28914 to be followed by the service technician. Another suchsub-assembly might include an industrial machine drive shaft 28904. AnRFID device may be disposed with the machine that may include details,such as those described herein for smart RFID devices, for the specificdrive shaft 28904. The drive shaft 28904 RFID device may communicate,such as through wireless communication with other devices brought intoproximity, such as a smart phone, tablet or the like 28914 so that auser of the table and the like 28914 may access the information storedon the drive shaft 28904 RFID device for conducting service,maintenance, testing, and the like. In embodiments, the drive shaft28904 service procedure may be retrieved from the drive shaft 28904 RFIDand displayed via an application executing on the table 28914 to befollowed by the service technician. Yet another such sub-assembly mightinclude an industrial machine gear box 28906. An RFID device may bedisposed with the machine that may include details, such as thosedescribed herein for smart RFID devices, for the specific gear box28906. The RFID device in the gear box 28906 device may communicate,such as through wireless communication with other devices brought intoproximity, such as a smart phone, tablet or the like 28914 so that auser of the table and the like 28914 may access the information storedon the gear box 28906 RFID device for conducting service, maintenance,testing, and the like. In embodiments, the gear box 28906 serviceprocedure may be retrieved from the gear box 28906 RFID and displayedvia an application executing on the table 28914 to be followed by theservice technician. Yet another such sub-assembly might include anindustrial machine articulated arm 28908. An RFID device may be disposedwith the machine that may include details, such as those describedherein for smart RFID devices, for the specific articulated arm 28908.The articulated arm 28908 RFID device may communicate, such as throughwireless communication with other devices brought into proximity, suchas a smart phone, tablet or the like 28914 so that a user of the tableand the like 28914 may access the information stored on the articulatedarm 28908 RFID device for conducting service, maintenance, testing, andthe like. In embodiments, the articulated arm 28908 service proceduremay be retrieved from the articulated arm 28908 RFID and displayed viaan application executing on the table 28914 to be followed by theservice technician.

Referring further to FIG. 305, yet another such sub-assembly mightinclude an industrial machine bucket 28910. An RFID device may bedisposed with the machine that may include details, such as thosedescribed herein for smart RFID devices, for the specific bucket 28910.The bucket 28910 RFID device may communicate, such as through wirelesscommunication with other devices brought into proximity, such as a smartphone, tablet or the like 28914 so that a user of the table and the like28914 may access the information stored on the bucket 28910 RFID devicefor conducting service, maintenance, testing, and the like. Inembodiments, another such sub-assembly might include an industrialmachine drive train 28912. An RFID device may be disposed with themachine that may include details, such as those described herein forsmart RFID devices, for the specific drive train 28912. The drive train28912 RFID device may communicate, such as through wirelesscommunication with other devices brought into proximity, such as a smartphone, tablet or the like 28914 so that a user of the table and the like28914 may access the information stored on the drive train 28912 RFIDdevice for conducting service, maintenance, testing, and the like. Inembodiments, the drive train 28912 service procedure may be retrievedfrom the drive train 28912 RFID and displayed via an applicationexecuting on the table 28914 to be followed by the service technician.In embodiments, any of the RFID devices, such as the motor 28902 RFID,the drive shaft 28904 RFID, the gear box 28906 RFID, the articulated arm28908 RFID, the bucket 28910 RFID, the drive train 28912 RFID and thelike may communicate via a wireless communication network with an accesspoint, such as industrial machine access point 28916 that may bedisposed on the industrial machine 28900 or proximal thereto.Communication from the RFID devices through the industrial machineaccess point 28916 to gain access to a network 28918, such as a networkfor connecting other industrial machines in a facility or externalnetworks such as the Internet. Information stored in the industrialmachine RFID devices may be transmitted over the network 28918 for usein the predictive maintenance methods and systems described herein.

In embodiments, a system may include a smart RFID element configured tocapture and store in a non-volatile computer-accessible memoryoperational, physical and diagnostic result information for a portion ofan industrial machine by communicatively coupling with at least onesensor configured to monitor a condition of the portion of theindustrial machine. The smart RFID element may further be configured toreceive, organize, and store in the non-volatile memory information thatenables execution of at least one service procedure for the industrialmachine.

In embodiments, information about an industrial machine, such as about aportion of the industrial machine may be stored in an RFID elementdisposed with the industrial machine or portion thereof. The informationstored may be configured to facilitate rapid random access to anyportion of the information quickly and efficiently, such as through useof a smart phone or other computing device configured with at least aweb browser and the like. The information may be configured as one ormore data structures, such as a hierarchical data structure and the likethat may also facilitate exploration of the information through browsingthe hierarchy and the like. Referring to FIG. 306, an exemplary highlevel structure 29000 of a portion of such an RFID is presented andincludes rows and columns. The exemplary high level structure 29000 mayinclude a category of information 29002 that may identify a general areaof information, such as production and the like. Each such category maybe described in a description column 29004 that may have furtheridentifying information. A notes column 29006 may be configured withfree-form notes that may be updated as needed. In embodiments, thecategory 29002 may include a range of information categories associatedwith the industrial machine, such as Production, Parts, Quality,Installation, Validation, Procedures, Operational, Assembly and thelike. In an example of the category 29002, validation 29008 may includea list of validation tests that are required and that are performed,along with results. Validation tests may be performed to validateinstallation at a customer site and the like. Validation 29008 may alsoinclude links to one or more procedures accessible in the RFID throughthe procedures 29010 category that are required for validation.

In embodiments, industrial machine-related information that may bestored on and/or accessible via a smart RFID element may include,without limitation operational data collected by sensors deployed withthe industrial machine and collected via the sensor data collectionmethods and systems described and the references incorporated herein.Other information that may be stored on or accessible from a smart RFIDelement may include, without limitation detected exceptions inoperational and/or test data, such as excess temperatures, unexpectedshutdowns, system restarts, and the like. A smart RFID element maycommunicate with an external computing device, such as a smart phone,tablet, communication infrastructure node, computer, mesh networkdevice, and the like via a range of communication protocols includingWiFi, NFC, BLUETOOTH and others. In embodiments, a smart RFID elementmay communicate wirelessly with a portable computing device when thecomputing device is in wireless communication proximity, such as when aportable computing device is brought within NFC range of the smart RFIDelement. A smart RFID element may communicate over a network, such asthe Internet as an IoT device. The smart RFID element may send data to aserver, such as a web server or the like that may aggregate informationfrom the element and cloud-accessible sources for one or more serviceactivities associated with the industrial machine. In embodiments, asmart RFID element may communicate with external computing device(s) atconvenient times, such as at the end/start of an activity, shift, day,when preventive maintenance is soon to be performed, and the like.

A smart RFID element may be used during production and/or assembly of anindustrial machine or portion thereof to capture physical details of themachine, such as for bearing frequency, gear teeth count and type,build/assembly version information, build/test parameters, self-testinformation, calibration information, test time, inventory dwell time,and the like.

A smart RFID element may be used during installation and/or deploymentof an industrial machine or portion thereof to capture orientation ofthe machine, testing activity, start-up activity, validationactivity/runs, production start time,installation/deployment/configuration personnel, images of theindustrial machine, and the like, at least a portion of which may bedetermined by one or more installation and/or deployment procedures thatmay be stored on and/or accessible through the smart RFID element.

In embodiments, a system may include a smart RFID element configured tocapture and store in a non-volatile computer-accessible memoryoperational, physical and diagnostic result information for a portion ofan industrial machine by communicatively coupling with at least onesensor configured to monitor a condition of the portion of theindustrial machine. The smart RFID element may further be configured toreceive, organize, and store in the non-volatile memory information thatenables execution of at least one service procedure for the industrialmachine. The smart RFID may further be configured to facilitatehierarchical access to information about the industrial machine,including a plurality of portions directly accessible from a root entryfor the industrial machine. In embodiments, each of the plurality ofdirectly accessible portions is structured to store entries for oneportion selected from the list consisting of production information,parts information, quality information, installation information,validation information, procedure information, operational information,and assembly information

In embodiments, an alternate configuration of a smart RFID forindustrial machine information storage and access, such as for serviceand the like may include a data structure as depicted in FIG. 307. Datastructure 29100 may be organized as columns and rows as shown, and thelike. A first column may be a topic column 29102, such as productiontopics including, without limitation, date(s) of assembly, location,model number, serial number, time, work order number, customer, imagesof the industrial machine as built and the like. Each topic in the topiccolumn 29102 may have one or more corresponding values in a value column29104. In an example, a serial number topic 29108 in the topic column29102 may have one or more corresponding serial numbers for the specificindustrial machine listed in the value column 29104. Comments or othermeta data for each topic in the topic column 29102 may be captured incorresponding entries in a notes column 29110.

In embodiments, a system may include a smart RFID element configured tocapture and store in a non-volatile computer-accessible memoryoperational, physical and diagnostic result information for a portion ofan industrial machine by communicatively coupling with at least onesensor configured to monitor a condition of the portion of theindustrial machine. The smart RFID element may further be configured toreceive, organize, and store in the non-volatile memory information thatenables execution of at least one service procedure for the industrialmachine. In embodiments, the production portion may include entries forassembly date, assembly location, machine model number, machine serialnumber, machine assembly time, machine assembly work order number,customer, and images of portions of the industrial machine.

In embodiments, an alternate configuration of a smart RFID forindustrial machine information storage and access, such as for serviceand the like may include a procedure data structure as depicted in FIG.308. A machine-level procedure data structure 29200 may be organized ascolumns and rows as shown, and the like. A first column may be aprocedure column 29202 that may list machine-level procedures, such ascalibration, shutdown, regulatory compliance, assembly, safety-checking,image capture and the like. Each procedure in the machine-levelprocedure column 29202 may have one or more corresponding values in anattribute column 29204, such as a procedure identification number, aversion, and the like. In an example, a safety check procedure 29208entry in the procedure column 29202 may have one or more correspondingprocedure number(s) and corresponding version number(s) in the column29204. Comments or other meta data for each procedure in the procedurecolumn 29202 may be captured in corresponding entries in a notes column29210.

In embodiments, a system may include a smart RFID element configured tocapture and store in a non-volatile computer-accessible memoryoperational, physical and diagnostic result information for a portion ofan industrial machine by communicatively coupling with at least onesensor configured to monitor a condition of the portion of theindustrial machine. The smart RFID element may further be configured toreceive, organize, and store in the non-volatile memory information thatenables execution of at least one service procedure for the industrialmachine. In embodiments, the procedure portion may include entries forprocedures selected from the list consisting of calibration, shutdown,regulatory, assembly, safety check, image capture, preventivemaintenance, part repair, part replacement, and disassembly.

In embodiments, referring to FIG. 309, methods and systems forcollecting information 29300 about an industrial machine 29320, such asinformation about the machine operation, conditions, and the like may bebeneficial to industrial machine predictive maintenance methods andsystems, such as those described herein and elsewhere. In embodiments,collecting the information from sensors on an industrial machine mayinclude routing the collected information through one or more accesspoints 29308 to a networked server 29318 where the information may beprocessed and stored. In embodiments, collecting information fromsensors on an industrial machine may include communicating betweensensors and a smart RFID device 29302 disposed on or with the machine.Data from sensors, such as temperature sensors 29310, vibration sensors29312, rotation sensors 29314, operational cycle sensors (e.g., cyclecounters and the like) 29316 may be provided to a smart RFID device29302 where the information may be processed and stored for furtheraccess by an external device, such as the server 29318, a handled device(not shown) brought into communication proximity of the industrialmachine 29320, and the like. Industrial machine-specific data may becollected from the sensors and routed to one or more web servers 29318that may employ a processor 29306 to generate a digital twin 29304 ofthe smart RFID 29302 on a computer accessible memory other than thesmart RFID 29302. In embodiments, the digital twin 29304 may begenerated by copying content in the smart RFID 29302. Likewise,machine-specific sensed data may be copied from the RFID twin 29304memory to the smart RFID device 29302. Therefore, the RFID twin 29304may be a copy of the smart RFID 29302, may be created independently ofthe smart RFID 29302, while maintaining a compatible structure, format,and substantively identical content, or may be a source ofmachine-specific data (e.g., as provided from the sensors over theaccess point) that may be copied to the smart RFID 29302 to maintain acopy of the information on the machine. In embodiments, server 29318 maymaintain a digital twin of a plurality of smart RFID devices for aplurality of industrial machines, including multiple smart RFID devicesfor a single industrial machine and the like.

In embodiments, a system may include a smart RFID element configured tocapture and store in a non-volatile computer-accessible memoryoperational, physical and diagnostic result information for a portion ofan industrial machine by communicatively coupling with at least onesensor configured to monitor a condition of the portion of theindustrial machine. The smart RFID element may further be configured toreceive, organize, and store in the non-volatile memory information thatenables execution of at least one service procedure for the industrialmachine. In embodiments, the system above may also include a datastorage element accessible through a processor, the data storage elementcomprising a copy of information stored in a plurality of the smart RFIDelement. In embodiments, each copy of information comprises a twin ofthe information stored in the corresponding smart RFID

In embodiments, industrial machine predictive maintenance methods andsystems, such as those described herein may include use of one or moremachine-resident smart RFID data structures that may capture informationrelated to planning, engineering, production, assembly, testing and thelike of portions of the industrial machine. Embodiments that mayfacilitate capturing information from these processes may be depicted inFIG. 310. An industrial machine 29422 may comprise several elements,such as operational elements, structural elements, processing elements,and at least one smart RFID element 29402. During production of theindustrial machine 29422, an industrial machine-resident processor 29408may work cooperatively with self-test elements 29424 and the like toperform testing of the industrial machine. Data collected duringself-testing, such as confirmation of proper operation and the like maybe stored in the smart RFID element 29402, such as by the processorwriting this data into a memory of the smart RFID element 29402. Inembodiments, a production test system 29418 may also perform testing ofportions of the industrial machine 29422, the results of which may bestored on the smart RFID element 29402. The industrial machine 29422 maycommunicate with a production network 29420, such as an intranet and thelike during production to gather and/or provide information for variousproduction systems, such as quality systems 29410, manufacturingresource and planning (MRP) systems 29414, production engineeringsystems 29416 and the like. Information, such as parts lists, productioninformation, and the like, an example data structure of which isdepicted in FIG. 307, may be stored with the smart RFID element 29402,such as by the industrial machine 29422 communicating over theproduction network 29420 via a production access point 29412 and thelike. Information from the various production systems, quality 29410,MRP 29414, engineering system 29416, testing 29418 and the like may betransferred over the network 29420 to the smart RFID element 29402. Inembodiments, a networked server 29426 may communicate with at least aportion of these production systems over the network 29420 to, forexample capture and process with a processor 29406 relevant productioninformation to be stored in the smart RFID element 29402 and/or in adata structure in a memory accessible to the server 29426. A datastructure 29404 may include at least a portion of the information storedin the smart RFID element 29402. In embodiments, the data structure29404 may be a digital twin of at least the relevant production contentof the smart RFID element 29402 for the specific industrial machinebeing produced. In embodiments, data from the production systems mayflow through the network 29420 to the server 29426 and may optionally beprocessed there, such as to be formatted, encoded, and the like anddelivered, such as over a wireless connection to the industrial machine29422 for storing with the smart RFID 29402. Production systems mayinclude the quality control systems 29410 that may include capturingimages of parts, sub-assemblies, and portions of the industrial machine.Images captured may be processed with machine vision and other imageanalysis technologies to validate assembly and the like. These images,image analysis data derived from these images, and the like may bestored so that it may be accessed through the smart RFID element 29402.In an example, procedures such as test procedures used in production maybe useful for testing the industrial machine 29422 as part of adeployment process. These procedures may be communicated from one of theproduction systems, such as the engineering system 29416 over theproduction network 29420, eventually to be stored on the smart RFID29402, the digital twin 29404 or both. This may satisfy a goal of themethods and systems described herein of facilitating access toindustrial machine-specific procedures via a smart RFID element on eachindustrial machine.

In embodiments, production information stored in, for example the smartRFID element 29402 may be useful to procedures that are to be followedduring installation, calibration, repair, preventive maintenance and thelike. In an example, certain test results may indicate an operationalmargin (e.g., maximum and/or minimum values) verified during production.These results may be useful during validating testing of a deployment ofthe industrial machine to facilitate confirming the deployment continuesto meet expectations. By making this and other production and industrialmachine information available during installation and other deployedprocedures, the machine-resident smart RFID element 29402 reducesinterdependency of production and related systems once an industrialmachine leaves the production environment. In an example, a procedurefor testing a portion of the industrial machine may be stored in thesmart RFID element. Test results that correspond to that procedure mayalso be stored therein. Therefore, even if the specific procedure ismodified for subsequently produced industrial machines, it may bepossible to perform tests associated with the specific procedure used toproduce the specific industrial machine; thereby saving time andconfusion that may occur when a new test procedure is used, but oldprocedure test results are expected to be met.

In embodiments, a method of configuring production data in a smart RFIDof an industrial machine may include configuring a smart RFID with aportion of an industrial machine to capture and store in a non-volatilecomputer-accessible memory operational, physical and diagnostic resultinformation for a corresponding portion of the industrial machine. Themethod may include communicatively coupling the smart RFID with aprocessor of the industrial machine and at least one sensor configuredto monitor a condition of the portion of the industrial machine. Themethod may further include executing with the processor a self-test ofthe portion of the industrial machine and storing in the smart RFID aresult of the self-test. The method may yet further include coupling theindustrial machine through a production access point to a network oftesting systems and an industrial machine production server. The methodmay further include performing production tests on the portion of theindustrial machine with the testing systems, a result of which is storedin duplicate on the smart RFID and in a data storage facility accessibleby a processor of the production server. In embodiments, the duplicateof the testing results stored in the data storage facility may be a twinof the corresponding portion of the smart RFID.

In embodiments, a marketplace of industrial machine parts, services,tools, materials and the like may be maintained through a combination ofa CMMS control system, and third parties each providing informationabout services, parts, tools, materials, costs, and logistics that theyprovide. Such a marketplace may be cloud-based so that access to thisinformation, can be made available to participants including industrialmachine owners and the like. In embodiments, a representative embodimentis depicted in FIG. 311. A CMMS system 29502 for managing at least partand service orders for required services may act as a control gateway toa marketplace 29512 for industrial machine owners 29524 and the like.The CMMS system 29502 may include managing bids and orders for parts,service, tools, materials and other aspects of industrial machineservice and maintenance. Exemplary CMMS subsystems, systems, facilitiesand the like are described elsewhere herein. In the embodiment of FIG.311, the CMMS system 29502 may further maintain and update order historydetails 29510. These details may include information descriptive of theparts, services, and the like that may be ordered. Details may includehistorical pricing, logistics requirements and costs, order lead times,and other factors that may be useful when managing information in themarketplace 29512. In an example, a part supplier 29508 may offer a partfor sale in the marketplace. Historical pricing for the part based onthe order details 29510 may be used to recommend a price at which thepart supplier 29508 should offer the part. In another example, the partsupplier 29508 may offer availability of a part with a 2-day lead time.However, the historical details 29510 may indicate that this supplier29508 is underestimating the time required to provide the part and mayfacilitate incorporating a proper lead time when placing the order sothat the part can be ordered only when needed but with sufficient leadtime for it to be available when a service that requires the part isscheduled to be performed. Such information management may be implicitmanagement because it is based on actual performance rather than merestatements by a provider.

In embodiments, service providers 29506 may configure offering for a setof services 29516 that meet their technical expertise. The serviceproviders 29506 may directly configure and update this set of servicesover time so that it reflects the services available from eachindividual service provider 29506 over time. Likewise, the partssupplier 29508 may configure and maintain a list of parts 29514 forindustrial machines that the supplier offers. Information such asavailability (e.g., local inventory, lead time, and the like) may bedirectly maintained by the parts supplier 29508. The CMMS system 29502may access his and related information in the marketplace 29512 whenconfiguring an order for parts, services, and the like. Similarly,suppliers of tools may configure information regarding industrialmachine service tools 29520 and suppliers of materials may configure andmaintain information regarding industrial machine service materials29522 (e.g., lubricants, other consumable items, and the like).

In embodiments, parts manufacturers 29504 may also provide and maintaininformation regarding parts that they provide, such as replacementparts, add-ons, upgrades, complete systems, subsystems, accessories andthe like to the marketplace.

In embodiments, a logistics suppliers 29518, such as shippers and thelike, may provide and maintain a set of logistics services in themarketplace that they provide for industrial machine maintenance parts,services and the like. The logistics supplier 29518 may offer deliveryservices in different geographic regions and may use information such aslocation of the industrial machine to establish rates and servicesavailable in the relevant region.

In embodiments, an industrial machine predictive maintenance system mayform a marketplace that includes a plurality of parts supplier computingsystems configured to maintain industrial machine service marketplaceinformation about industrial machine parts offered for sale. Themarketplace may include a plurality of service provider computingsystems configured to maintain industrial machine service marketplaceinformation about industrial machine services offered. The marketplacemay further include at least one computerized maintenance managementsystem (CMMS) that is configured to facilitate access to at least one ofservices, parts, materials, and tools offered in the marketplaceresponsive to an industrial machine maintenance recommendation providedby an industrial machine predictive maintenance system. The marketplacemay yet further include a plurality of logistics provider computingsystems configured to maintain industrial machine service marketplaceinformation for at least one of shipping and logistics services offeredin the marketplace. Further in embodiments, each of the plurality ofparts suppliers, service providers, and logistics providers maintaincorresponding information for their offerings directly in themarketplace via at least one Application Programming Interface of themarketplace. The market place may further include a CMMS that adaptsofferings of parts, services, and logistics to industrial machine ownersbased on norms established from analysis of prior orders for parts,services and logistics.

In embodiments, a distributed ledger for tracking field serviceactivities, including predicative maintenance activities and the likethat are performed on industrial machines is depicted in FIG. 312.Methods and systems that are disclosed herein for an industrial machinemaintenance distributed ledger may include a distributed ledger 29602supporting the tracking of predictive maintenance activities executed inan automated industrial machine predictive maintenance eco-system 29600.Embodiments may include a self-organizing data collector 29608 that isconfigured to distribute collected information to the distributed ledger29602. Embodiments may include a network-sensitive data collector thatis configured to distribute collected information to a distributedledger based on network conditions. Embodiments may include a remotelyorganized data collector that is configured to distribute collectedinformation to a distributed ledger based on intelligent, remotemanagement of the distribution. Embodiments may include a data collectorwith self-organizing local storage that is configured to distributecollected information to a distributed ledger. Embodiments may includethe system 29600 for industrial machine maintenance-related datacollection in an industrial environment using a distributed ledger fordata storage and self-organizing network coding for data transport. Inembodiments, data storage may be of a data structure that supports ahaptic interface for data presentation, a heat map interface for datapresentation, and/or an interface that operates with self-organizedtuning of an interface layer.

In embodiments, storage of service and maintenance information, whichmay include services, parts, service providers, records for specificindustrial machines, analytics generated from the service andmaintenance information and the like may include the one or distributeledger 29602 instances in various elements of the system 29600. In anexample, the distributed ledger 29602 may facilitate access to all ofthe information available in the distributed ledger 29602 withoutrelying on any one network server, node, or the like due at least inpart to some portion of the information being distributed and optionallyduplicated on distinct portions of a network, such as the Internet. Thedistributed ledger 29602 may be distributed among elements in anindustrial machine maintenance platform including, without limitation,the industrial machine data analysis system 28602, the industrialmachine predictive maintenance system 2966, the CMMS system 28622, theservice delivery and tracking system 28630, the industrial machine29604, the industrial facility computing system 29606, the cloud-basedstorage 29616, and the like.

In embodiments, information stored in the distributed ledger 29602 maybe generated by and/or adjusted based on artificial intelligence 29610,such as machine learning algorithms that process the information fromwhich the distributed ledger is sourced.

In embodiments, the methods and systems that may support distributedledger embodiments may include role-based access control 29614 of and tothe distributed ledger data. Exemplary roles 29612 that may be managedby a distributed ledger control facility may include: an owner role,which may be an industrial machine leasing company, individual ordirect-use buyer entity or individual; an operator role, which may be anentity or individual that is responsible for day to day operation of anindustrial machine, such as a company that provides a service using theindustrial machine, a lessor of the machine, and the like; a lessorrole, which may be an entity or individual that has a term-based orotherwise limited lease of an industrial machine; a manufacturer role,which may be an entity or individual that produced some portion of themachine and that may have limited access to, for example, informationpertaining to the portion produced; a part supplier role, which may bean entity or individual that provides some part(s) for manufacturer,service, upgrade, maintenance, refurbishing, or other functions and mayprovide OEM and/or after-market parts for an industrial machine; aservice provider, which may be an individual or entity that providesservices, such as contracts for preventive maintenance and repair,emergency repair, upgrades and the like; a service broker role, whichmay be an entity or individual that facilitates service needs, such as aregional entity that facilitates automated service activities inregions, such as specific countries and that may be required to belicensed, registered, and the like in the specific country and that mayact comparably to a general contractor, providing oversight and warrantyfor work done by 3^(rd) parties, such a role may be valuable when amachine has been installed per local rules, and the like that is outsideof the scope of what an automated service identification system mayhandle; a regulatory role, which maybe a government or other authorityentity or individual that may conduct inspections and the like and maybe limited to access certain data required for ensuring compliance withregulations and the like for activities such as preventive maintenance,use of authorized parts/service providers, auditing, and the like.

In embodiments, a predictive maintenance platform may use a securearchitecture for tracking and resolving transactions, such as adistributed ledger. In embodiments, transactions in data packages aretracked in a chained, distributed data structure, such as a Blockchain™,allowing forensic analysis and validation where individual devices storea portion of the ledger representing transactions in data packages. Thedistributed ledger may be distributed to IoT devices, to web servers, toindustrial machine maintenance transaction record storage facilities,and the like, so that maintenance and related information can beverified without reliance on a single, central repository ofinformation. The platform may be configured to store data in thedistributed ledger and to retrieve data from it (and from constituentdevices) in order to resolve service transactions, such as parts andservice orders, and the like. Thus, a distributed ledger for handlingdata for maintenance-related transactions is provided. In embodiments, aself-organizing storage system may be used for optimizing storage ofdistributed ledger data, as well as for organizing storage of packagesof data, such as IoT data, industrial machine maintenance data, partsand service data, knowledgeable worker data, and the like.

In embodiments, a system may include a plurality of computing systemsconfigured to perform one or more predictive maintenance actions. Inembodiments, a portion of the plurality of computing systems connectedvia a peer-to-peer communication network. A record of industrial machinemaintenance actions including a portion of the predictive maintenanceactions may be maintained by the portion of the plurality of computingsystems as a distributed ledger. In embodiments, a computing system ofthe portion of computing systems performs at least one industrialmachine maintenance role selected from the list consisting of industrialmachine data analysis, industrial machine predictive maintenancerecommendations, industrial machine maintenance order management,delivery and tracking of service actions, industrial machine servicescheduling, and contributes a result of it performing the at least oneindustrial machine maintenance to the record.

In embodiments, a system may include a plurality of computing systemsconfigured to perform one or more predictive maintenance actions. Inembodiments, a portion of the plurality of computing systems areconnected via a peer-to-peer communication network. In embodiments, thesystem may further include a role-based control facility for accessing arecord of industrial machine maintenance actions, the record including aportion of the predictive maintenance actions.

In embodiments, the portion of the plurality of computing systemsoperate the record as a distributed ledger.

In embodiments, methods and systems for operating a predictivemaintenance analysis and control system may benefit from visualinformation as well as performance and operational data from industrialsensors and the like deployed with an industrial machine. Visualinformation, such as images captured about individual parts, assemblies,process steps, machine conditions and the like may be analyzed withmachine vision and other techniques, including human viewing andassessment, to determine conditions that may impact prediction of aservice need or the like. Generating and maintaining an updated accurateimage library of visual information for industrial machines may bebenefited from service personnel capturing images of portions of eachindustrial machine under various conditions, including withoutlimitation operating, testing, and non-operating conditions (e.g.,during service, maintenance, repair, upgrade, and refurbishing machinestates). In embodiments, a system to facilitate capture of images isdepicted in FIG. 313. A procedure for industrial machine service orrepair 29716 may be identified for a scheduled service of the machine.The procedure 29716 may include a set of steps to be taken to performthe scheduled service activity. One or more of the steps may includecapturing image(s) of portions of the industrial machine, such as anexternal view depicting the machine in its deployed environment, a viewof a part to be replaced, a view depicting a condition of gears,bearings, support structures, housings and the like. While a proceduremay include capturing image(s), learning from service techniciansperforming the procedure may be incorporated into implementing theprocedure using a preventive maintenance system 29724 that uses machinelearning and other techniques to facilitate augmenting and/or adjustingimage capture steps in a procedure and the like. The predictivemaintenance system 29724 may provide information, such as in the form ofconditions that suggest an image should be captured that may not bedirectly required in a procedure. Such a case may arise when thepredictive maintenance system 29724 learns that certain bearings exhibitwear that is visible before the bearing fails. The length of time that abearing can operate under various conditions may not be a sufficientindicator to perform a service, whereas an image with visual indicationof such wear would be sufficient. Therefore, when a service technicianperforms a service procedure that does not include capturing an image ofthe certain bearings, the technician may be directed to capture an imageof these certain bearings. This may be indicated to the servicetechnician as a service alert, such as a general posting.

However, information about the visual condition and timing of a serviceactivity may be used to facilitate augmenting/updating a procedure, suchas the procedure 29716 to include capturing one or more images of thecertain bearings.

In embodiments, information from the predictive maintenance system 29724may be processed by an image capture triggering facility 29722 toprovide an indication to a procedure updating facility 29702 that anupdate to the procedure, such as to add capturing an image of thecertain bearings, is required. This indication may be combined withimage capture timing information that may be provided to the procedureupdate facility 29702 from an image capture timing facility 29720 thatmay use industrial machine use and service schedule information 29726 tocreate a window of time in which the certain bearings are expected to beavailable to be imaged. Such a window of time may include scheduledservice and/or maintenance activities during which the machine may beoff-line. Such a window of time may include planned operational timesduring which the machine will be operating. A potential goal of suchwindow generation may be to capture image(s) of the certain bearingsduring a planned service visit, to avoid machine shut downs specificallyto capture the image(s), despite the images being required before aservice activity in which the bearings would normally be images isexecuted, such as a scheduled preventive maintenance activity to inspectthe bearings and the like.

In embodiments, when the existing procedure 29716 is to be appliedduring an image capture window output from the image capture timingfacility 29720, the image capture triggering facility 29722 output maybe checked. If the image capture triggering facility 29722 indicatesthat an image is required, the procedure may be updated by the procedureupdate facility 29702, such as by adding a step to the procedure,changing an imaging target (e.g., from a part to the bearings) for anexisting image capture step, and the like.

In embodiments, the revised procedure 29702 may be followed by theservice technician. When a step that has been added/augmented to capturean image of the certain bearings is to be performed, an image capturetemplate 29704 may be presented to the technician to aid in capturingthe proper image. Likewise, and as described elsewhere herein, anaugmented reality application may be executed as part of such an imagecapture step to further aid the service technician in capturing theproper image. In embodiments, a machine vision system 29708 and otherimage analysis techniques may be used to suggest refinements and/orconfirm the captured image meets the requirements for facilitatingdetecting the visual condition of the certain bearings, and the like.

In embodiments, an image capture reward facility 29714 may interfacewith the updated procedure 29718 and/or the service technician tofacilitate incentivizing the service technician to capture an acceptableimage. Such a reward facility 29714 may include a range of rewards fromdirect monetary rewards to positive ratings for the service technician,which may ultimately increase the technician's value and consequentlycompensation.

Captured images, such as those that are accepted by the machine visionsystem 29708 and the like, may be stored in a smart RFID element 29710of the industrial machine, transferred through the image capture device(e.g., a camera-enabled smart phone, and the like) to the Smart RFID andto one or more nodes in a distributed ledger of preventive maintenancedata.

In embodiments, a method of image capture of a portion of an industrialmachine includes updating a procedure for performing a service thatimplements a predicted maintenance action on an industrial machine, theupdating responsive to a trigger condition for capturing an image of aportion of the industrial machine being met. The method of image capturemay further include providing an image capture template in an electronicdisplay overlaying a live image of a portion of the industrial machineto facilitate image capture, applying augmented reality that indicates adegree of alignment of the live image with the template, examining animage captured using the updated procedure with machine vision todetermine at least one part of the machine present in the capturedimage, and responsive to a result of the machine vision examination,operating an image capture reward facility to generate a reward for thecaptured image. In embodiments, the updating may be responsive to atrigger condition that is based on analysis of industrial machinefailure data such that the analysis suggests capturing an image that isnot specified in the procedure prior to the updating step. Inembodiments, the updating may be responsive to the procedure forperforming the service being performed on an industrial machine thatmeets a predictive maintenance criterion associated with the portion ofthe industrial machine for which an image is to be captured. Inembodiments, the trigger condition may include a type of industrialmachine associated with the industrial machine for which a serviceprocedure is being performed and a duration of time since the portion ofthe industrial was captured in an image.

In embodiments, an industrial machine predictive maintenancefacilitating system may apply machine learning to images of industrialmachines captured during operations such as assembly, testing,servicing, repair, upgrading, scheduled maintenance, preventivemaintenance, and the like. The machine learning may be applied to theimages and/or data derived from the images using algorithms such asimage analysis algorithms, part detection algorithms, machine vision andthe like to facilitate improving machine-automated detection of portionsof the industrial machine, such as individual parts, subassemblies andthe like. In embodiments, machine-automated detection of parts,subassemblies and the like may provide information to the methods andsystems here including, without limitation, predictive maintenanceprocesses, service provider rating methods, procedure rating methods,inventory management systems, maintenance scheduling (e.g., if amaintenance operation should be scheduled sooner than previouslyestimated, and the like).

In embodiments, methods and systems for machine-automated detection ofparts of an industrial machine may include image capture, processing,analysis, learning and automation steps, such as those exemplarilydepicted in FIG. 314. In embodiments, a method for automaticallydetecting parts of an industrial machine may start with capturing animage step 29802. Alternatively, image data from previously capturedimages may be accessed from a data store of images, such as a databaseand the like. The image capture step 29802 may be performed, such as bya service technician and the like in association with performing aservice operation, such as a maintenance procedure, repair procedure,upgrade procedure and the like. The image capture step 29802 may beinformed by a procedure or the like that may indicate a target part tobe imaged, a template thereof, and the like. A procedure, target part,template and the like may be retrieved from an image capture guidancedata storage 29804. In embodiments, a procedure may include a specificinstruction to use a part image capture process and photograph one ormore parts indicated by the procedure. In an example, a procedure forservicing bearings of an industrial machine may include a step ofphotographing a shaft that the bearings handle and the like. Theprocedure may present on an electronic display of an image capturedevice, such as a tablet or smart phone and the like an imagerepresentative of the image to be captured. Such an image may be a mostrecent image captured of the specific industrial machine that may, forexample, be retrieved from an image data structure of a smart RFIDelement deployed with the industrial machine (e.g., a smart RFID elementconfigured with the portion of the machine that includes the bearings,shaft and the like). Such an image may be augmented with information,such as relative position of the camera through which the image wascaptured, time/date information, procedure number followed, and thelike. In embodiments, such an image may be processed into a template(e.g., coloring book/outline image, and the like) that facilitatesmanually aligning the image capture device. In embodiments, such atemplate may be an active template that processes an image visiblethrough the image capture device and provides indicators, such as colorchanges and the like of the template to further facilitate alignment ofthe image capture device. The active template may start with black (orsome other color) outlines of the object(s) to be captured withvertexes, edges, and the like turning green (or some different color)when alignment of the relevant vertex, edge and the like is sufficientto facilitate machine-automated detection of the part.

In embodiments, an image captured in the image capture step 29802 may beprocessed through an image validation step 29806 that may perform imageanalysis functions, such as for example comparing the image captureswith a reference image, such as one that may be retrieved from orderived from information in the image capture guidance data store 29804and the like. In embodiments, the captured image may be processed toimprove contrast and the like and compared during the validate imagecapture step 29806 with a most recently captured image from the smartRFID element disposed with the industrial machine through, for examplean image subtraction process, to determine if the captured image may bevalidated. An image that is not validated may be discarded and the usermay be directed back to the capture image step 29802 to capture anotherimage.

In embodiments, an image that may be validated in step 29806 may bepassed onto an image analysis or a similar step 29808 that may processimage analysis rules 29810 to detect one or more candidate parts fromthe validated image. Candidate parts may be stored in a candidate partsdata structure 29814 for further use. In embodiments, images ofcandidate parts in the candidate parts data structure 29814 may beretained for further training of machine learning algorithms thatfacilitate improving machine automated part detection from images. Inembodiments, images of candidate parts may be used in an instance of themachine automated parts detection flow 29800 of FIG. 314 and thendiscarded, erased, and the like. In embodiments, the image analysisrules 29810 may include data provided from the machine learning step29820, such as in the form of feedback and the like that may improveimage analysis of marginal images, such as those with poor contrast,unexpected content (e.g., excessive solvents, moving parts, reflectiveparts, and the like).

In embodiments, the one or more candidate parts of the candidate partsdata structure 29814 may be processed by a parts recognition algorithmstep 29816 that may perform, among other things, machine automated partsrecognition. An automated parts recognition algorithm may includegenerating attributes of candidate parts, such as dimensions and thelike that may be compared with part descriptive information that may beretrieved from a smart RFID data storage 29812, and the like. In anexample, a candidate part may be processed to detect edges and the likethat may be processed with automated measurement algorithms. Theresulting measurements may be used to determine a specific part from alibrary of parts for the specific industrial machine that may beavailable to the parts recognition algorithm 29816 in the RFID datastorage 29812 and the like. The specific part information may beretrieved from a production data system, such as a parts list, MRPsystem and the like and stored in the RFID data storage 29812 during aproduction operation, such as the exemplary production flow depicted inFIG. 293.

In embodiments, one or more results of the parts recognition algorithm29816 may be forwarded to a machine learning facility, that may executeone or more machine learning algorithms 29820 that may improve variousaspects of machine-automated part detection including, withoutlimitation, the image capture process 29802, the image validationprocess 29806, the image analysis process 29808, the part recognitionprocess 29816 and the like. In an example, part recognition process29816 may provide images of one or more candidate parts, a correspondingreference part, related attributes and the like, information extractedduring the parts recognition process, and the like to the machinelearning process 29820. The machine learning process may apply machinelearning techniques to facilitate determining aspects of candidatepart(s) that represent the best candidates for the correspondingreference part and provide feedback to at least the part recognitionprocess 29816 to improve part detection and the like.

In embodiments, information descriptive of recognized parts may bestored in an updated smart RFID element 29818, an updated server-baseddata structure 29822 comparable thereto, and the like. Informationstored may include one or more candidate part images, an identifier of areference part, recognition data, procedure number followed to capturethe image, and the like.

In embodiments, a method of machine learning-based part recognition mayinclude applying a target part imaging template to an image validatingprocedure that determines if an image captured meets an image capturevalidation criterion. The method may further include performing imageanalysis by processing a captured image with image analysis rules thatfacilitate detecting candidate parts of an industrial machine beingpresent in an image. In embodiments, recognizing one or more parts ofthe set of candidate parts as a part of the industrial machine based onsimilarity of a candidate part with images of parts of the specificindustrial machine may be included. Additionally, adapting at least oneof the target part template, the image analysis rules, and the partrecognition based on feedback produced from machine learning of therecognized parts, thereby improving at least one of image capture, imageanalysis and part recognition may be included in the method.

In embodiments, information gathered and generated for industrialmachine maintenance lifecycles, including predictive maintenance,manufacturer required maintenance, failure repairs, parts and serviceofferings and ordering, follow-up to maintenance activities, assessmentof procedures and service providers, failure rate and predictionanalysis, worker training, experience, and ratings, and the like may becaptured throughout the service lifecycle, processed with artificialintelligence and other machine learning-type algorithms and accumulatedin a database, such as a data model, linked database, columnar database,and the like. FIG. 315 depicts such a set of data embodied as aknowledge graph 29902. In embodiments, information about industrialmachines, such as parts, images, configurations, internal structures,use schedules, and the like may be processed by artificialintelligence-type functions 29906 (e.g., machine learning algorithms andthe like) along with information from other sources including withoutlimitation service information, failure information, worker-relatedinformation and the like. The information processing algorithms, such asinformation associative algorithms executed in exemplary artificialintelligence facility 29906 may cause portions of the predictivemaintenance and industrial machine service knowledge graph 29902 to beupdated, such as by establishing, changing, removing, strengthening andthe like knowledge graph node links 29916 among data nodes 29918;adding, updating, splitting and the like the data nodes 29918 toinitiate and refine a graph-based understanding of the relationshipsamong facts, know-how, analysis results and the like that influenceaspects of predictive maintenance processes, such as those describedherein.

In embodiments, information about machines may be processed and storedin machine data nodes 29908; information about failures may be processedand stored in failure data nodes 29910; information about industrialmachine service may be processed and stored in service data nodes 29912,information about workers for performing industrial machine service maybe processed and stored in worker data nodes 29914. Relationships amongdata nodes, such as a relationship between the machine data node 29908and the service data node 29912 may be depicted as the links 29916between nodes. A goal of initiating and updating such a knowledge graph,among other things may be to further improve for collecting,discovering, capturing, disseminating, managing, and processinginformation about industrial machines, including factual information(such as about internal structures, parts and components), operationalinformation and procedural information, including know-how and otherinformation relevant to maintenance, service and repairs.

In embodiments, as maintenance/service/repair/upgrade/installation andother industrial machine-related activities are performed, data aboutthe activities may be processed and used to enhance, augment, improve,refine, clarify, and correct the data nodes 29918, the relationshipsamong the nodes, and the like. In embodiments, preparing formaintenance/service/repair and other industrial machine activities maybenefit from the knowledge found in the knowledge graph 29902 andthereby improve efficiency, reduce computing complexity to generatesuitable service options, recommendations, orders and the like bytaking, for example an existing relationship between the failure node29910 and the worker node 29914 to efficiently identify a suitableworker for resolving the failure when it occurs on a specific machine.

In embodiments, improved methods and systems are provided herein forcollecting, discovering, capturing, disseminating, managing, andprocessing information about industrial machines, including factualinformation (such as about internal structures, parts and components),operational information and procedural information, including know-howand other information relevant to maintenance, service and repairs.These improved methods and systems may be provided with a predictivemaintenance knowledge system platform 30000 as depicted in FIG. 316. Apredictive maintenance knowledge system 30002 may facilitate collecting,discovering, capturing, disseminating, managing, and processinginformation about industrial machines, such as for facilitating serviceand maintenance thereof using the methods and systems described herein,including without limitation finding a set of workers having relevantknow-how and expertise about maintenance, service and repair of aparticular machine and finding, ordering, and fulfilling orders forrelevant parts and components, so that maintenance, service and repairoperations can occur seamlessly, with minimal disruption, and the like.The predictive maintenance knowledge system 30002 may interface with oneor more predictive maintenance knowledge bases and/or knowledge graphs30004. A knowledge base 30004 may further include or reference one ormore knowledge graphs that may contain information beneficial to themethods and systems that may be enabled by the predictive maintenanceknowledge system 30002. The predictive maintenance knowledge graph maycontain or provide computer access to information about industrialmachines, service activity of industrial machines, costs (e.g.,historical, trending, and predictive) for parts, materials, tools, andservices of industrial machines, algorithms and functionality foroperating the predictive maintenance knowledge system 30002, platform30000 and the like. In embodiments, the predictive maintenance knowledgesystem 30002 may process information from the predictive maintenanceknowledge base 30004 regarding expedited service charges that have beenimposed on certain instances of industrial machine service and develop aprice-time relationship that may aid in the decision by industrialmachine owners regarding service authorization and costs thereof. Anindustrial machine owner may be informed of the costs for expeditedservice and standard timing service to facilitate deciding if it isbetter to pay an expedite fee to have a maintenance function performedsoon while the machine is off-line for other reasons than to keep aschedule of the maintenance function that would require taking themachine off-line, such as in the near future. The predictive maintenanceknowledge system 30002 may facilitate coordination with serviceproviders, parts providers, material and tool providers and the likebased on the owner's decision so that the service can be performed inthe timeframe that the owner chooses.

In embodiments, specific industrial machine information may be stored inone or more smart RFID elements 30006 disposed with the specific machineand/or stored in a cloud-based data structure 30008 that may becompatible with (e.g., a backup, duplicate/twin, or other formatted datastructure). The predictive maintenance knowledge system 30002 may access(e.g., read data from and/or write data to) the RFID element(s) 30006,the cloud-based data structure 30008, and the like. Data read from thesmart RFID 30006/cloud-based structure 30008 may be specific to aparticular deployed industrial machine and may facilitate the methodsand systems for predictive maintenance and the like described hereinperforming coordination of resources to perform maintenance effectivelyand efficiently for the specific machine. In an example, a specificindustrial machine may have an operating cycle that results in greaterutilization of one of its moving parts (e.g., an industrial motor) thantypical. This knowledge may be used by the predictive maintenanceknowledge system 30002 to interact with service, parts, and materialsuppliers to provide a firm quote for performing a utilization-basedmaintenance service at a different time (e.g., weeks or months sooner)than other comparable industrial machines with lower utilization rates.

In embodiments, the predictive maintenance knowledge system 30002 mayexecute algorithms that gather information about a plurality ofindustrial machines, including a plurality of industrial machines ofdifferent types of machine (e.g., stationary machines, mobile machines,machines on vehicles, machines deployed at job sites, and the like)along with service provider information, parts and parts providerinformation, part location and inventory information, machine productionproviders, third-party parts handlers, logistics providers,transportation providers, service standards, service requirements,service activities including results of service and the like, and otherinformation to facilitate the predictive maintenance methods and systemsdescribed herein. One or more functions of the predictive maintenanceknowledge system 30002 may utilize service request information 30026,such as requests for service of a specific industrial machine and/or acollection of industrial machines from industrial machineowners/operators/providers/users to facilitate fulfilling those servicerequests. In embodiments, such service requests may become inputs to analgorithm that predicts when a service may be recommended for therequester, but also for comparable industrial machines. In an example,an industrial machine owner may request that a subset of industrialmachines at a job site receive a first service action. The predictivemaintenance knowledge system 30002 may use this request information andother information about the machines, such as their age and utilizationrate, to determine when the other industrial machines of the same typeas those for which the service is requested should be scheduled for acomparable service action.

In embodiments, in response to the specific service request 30026, thepredictive maintenance knowledge system 30002 may access information inthe smart RFID 30006 or its cloud-based backup 30008 to determine thespecific procedures involved, to determine what experience a potentialservice provide may need to perform the service. The predictivemaintenance knowledge system 30002 may access the knowledge base 30004to identify candidate service providers. Service providers that areknown to the predictive maintenance knowledge system 30002 (e.g., basedon, for example information in the knowledge base 30004) as havingsuccessfully demonstrated experience with the procedure needed for therequested service may be contacted to provide a service estimate 30036and/or a price estimate 30034 for service, parts, and the like.Similarly, parts and/or material that may be associated with theprocedure of the requested service may be identified. The predictivemaintenance knowledge system 30002 may also access the knowledge base30004 for sourcing information of the parts and/or material. Factorssuch as part cost, transportation costs, availability, location of theparts versus the machines, prior relationships between one or more partsproviders and a party associated with the service request, such as theindustrial machine owner and the like, and other factors may beevaluated to determine which parts provider to contact in preparationfor ordering the parts. With these factors considered, a part inquirymay be placed with one or more parts providers in anticipation of theservice being conducted by the qualified service provider as scheduled.The predictive maintenance knowledge system 30002 may respond to theservice request 30026 with one or more service recommendations 30032that may be associated with one or more price-based servicerecommendation options 30010 from which the requestor may choose. Inembodiments, the predictive maintenance knowledge system 30002 may haveenough information from the knowledge base 30004, responses to theservice estimate request 30036, and the like to automatically select aspecific price-based service recommendation 30010 from the options andmay, with or without requestor explicit approval, generate a serviceorder 30018, a parts/material/tools order 30016 if needed for therequested service 30026.

In embodiments, a service request and/or a predicted maintenanceactivity, and the like may be processed by the predictive maintenanceknowledge system 30002 and output a service funding recommendationand/or request 30012. Such a recommendation may include funding theservice from operating revenues, taking out a loan for the service,seeking third-party funding (e.g., industry sources, government grants,private funding sources, and the like). Such a request may includeproviding information to one or more third-parties about the requestedservice that may be used by the third-parties to submit a fundingproposal and/or response. In an example, an industrial machine thatprovides the public with clean water for a region may require a costlyservice. The predictive maintenance knowledge system 30002 may determinethat the specific industrial machine may be eligible for reimbursementfrom the federal government for at least a portion of the service. Arequest for funding by the federal government may be configured andactivated through the service funding 30012 and the like.

In embodiments, sources of information that the predictive maintenanceknowledge system 30002 may rely on may include information from serviceproviders 30024, information from parts providers 30022, informationfrom service material providers 30020, machine schedules 30030, incomingservice estimates and/or quotes 30028, and the like. A predictivemaintenance knowledge system 30002 may use service material providerinformation 30020 to determine price and availability of servicematerial. This information may be combined with service materialinventories of the requester (e.g., centralized, depot-based, or on-siteof the industrial machine), inventories of material of one or morequalified service providers and the like. In an example, if a serviceprovider has sufficient inventory of the required material accessiblelocal to the industrial machine for which service is required, but willneed to replenish that inventory after performing the service, thesystem may provide a recommendation to the service provider to have theservice material provider deliver the service material to the industrialmachine site in time for the schedule service. In an example, if theservice provider and the industrial machine owner does not haveinventory of the required service material, the predictive maintenanceknowledge system 30002 may generate an order with one of the servicematerial providers 30020 based on total price, availability, existingrelationships with the industrial machine owner and/or the serviceprovider and the like. In embodiments, at least a portion of theinventory of one or more of the service material providers 30020 may bedirectly managed by the predictive maintenance knowledge system 30002 sothat the predictive maintenance knowledge system 30002 may allocatematerial from the inventory for a service action. The service materialprovider 30020 may receive a notification from the predictivemaintenance knowledge system 30002 that they have been selected toprovide the material for the service action. Payment for the materialmay be made through a transaction facility associated with thepredictive maintenance knowledge system 30002 so that an operator of thepredictive maintenance knowledge system 30002 and the service materialprovider 30020 are compensated for their roles in this service action.Comparable examples may be envisioned for parts providers 30022, serviceprovider 30024, service funding sources (not shown), and the like.

In embodiments, the predictive maintenance knowledge system platform30000 may include a computerized maintenance management system (CMMS)30014 that may facilitate creating work orders, such as for maintenanceactions to resolve equipment problems, and the like. The CMMS 30014 mayfacilitate communicating parts and service requests to an EnterpriseResource Planning (ERP) system (not shown) that may facilitate handlingparts and service orders. In embodiments, an ERP system may beassociated with one or more of the owner/operator/provider/lessee/lessorof an industrial machine for which a service action is being coordinatedby the predictive maintenance knowledge system 30002. In embodiments,the CMMS 30014 may coordinate with the industrial machine owner's ERPsystem to effect placement of orders with the service provider, partsprovider, and the like.

In embodiments, a predictive maintenance system may include a predictivemaintenance knowledge system that facilitates collecting, discovering,capturing, disseminating, managing and processing information aboutindustrial machines to facilitate taking predictive maintenance actionson industrial machines. The knowledge system may include a plurality ofinterfaces for receiving information from service providers, partsproviders, material providers, machine use schedulers, a plurality ofinterfaces for sending information to service ordering facilities, partsordering facilities, service management facilities, service fundingfacilities, and a plurality of interfaces to smart RFID elements on aplurality of industrial machines. The predictive maintenance system mayfurther include a predictive maintenance knowledge graph thatfacilitates access by the predictive maintenance knowledge system toinformation about predictive maintenance service of industrial machinesthrough links among data domains including service providers, partsproviders, service requests, service estimates, machine schedules, andpredictions of maintenance activity. In embodiments, the predictivemaintenance knowledge system may generate at least one of servicerecommendations, price-based service options, price estimates, andservice estimates.

In embodiments, preventive maintenance and other scheduled maintenancefor industrial machines and the like may be scheduled at set intervalsbased on manufacturer's expectations regarding failure rates and thelike. By gathering and analyzing information about industrial machinesand the like, such as operational data, failure data, conditions foundduring preventive maintenance activities and the like, a new schedulefor maintenance activities may be configured that may further reduce theoccurrence of unplanned shutdowns due to part failure and the like. FIG.317 depicts a preventive maintenance schedule 30108 for a set ofbearings in a group of industrial machines 30102 that use the bearings.As presented, preventive maintenance events A, B, C, and D for thebearings are scheduled to occur at intervals over time for each of themachines. Data collected and analyzed by a predictive maintenance systemusing the methods and systems for predictive maintenance of industrialmachines as described herein may indicate that a different schedule ofbearing maintenance is needed to prevent failures. In the example ofFIG. 317, failures 30104 of machines 4 and 3 occur after preventivemaintenance activity B. In response there to, and when taking intoconsideration other factors, such as operating cycle rate of theindustrial machines, a new bearing maintenance schedule may beestablished for the machines. Since machines 1 and 2 have not yetfailed, a predictive maintenance event may be setup for machine 1 30110and for machine 2 30112. In embodiments, an operational rate of machine2 may be substantive less than machine 1; therefore, while both machinesuse the bearings that have failed in machines 3 and 4, a predictivemaintenance event schedule may be prepared individually for eachmachine. The predictive maintenance event for machine 1 30110 may be setto occur earlier than planned (event C) in the preventive maintenanceschedule 30108. An additional maintenance event for the machine 2 30112may be set to occur soon after the upcoming scheduled preventivemaintenance event (again event C) based on, for example timing offailure of machines 3 and 4 after preventive maintenance event B. Bysetting a shorter interval between preventive maintenance event C andpredictive maintenance event 2 (30112), a risk of a bearing-relatedfailure may be reduced.

In embodiments, an industrial machine predictive maintenance system mayapply machine learning and the like to a range of factors to facilitatepredicting and facilitating service, such as determining a schedule forservice, identifying at least one qualified party for performing theservice, recommending one or more sources of materials required for theservice, fulfilling procurement and delivery of the materials requiredfor the service, and rating the service of one or more parts of theindustrial machine. The machine learning capability of such a system maytake input, such as in the form of diagnostic-related information forthe industrial machine from one of a plurality of industrialmachine-related diagnostic test data, including without limitation atleast one of infrared thermography of one or more parts of theindustrial machine, ultrasonic testing of one or more parts of theindustrial machine, motor testing of one or more parts of the industrialmachine, magnetic field testing of the motor of one or more parts of theindustrial machine, electron magnetic flux (EMF) testing of one or moreparts of the industrial machine (e.g., pulse detection and the like),current and/or voltage testing of one or more parts of the industrialmachine (e.g., from machine resident testing equipment and/or externallyapplied testing equipment and the like), torsional testing of one ormore parts of the industrial machine (e.g., using EMF and the like),non-destructive testing of one or more parts of the industrial machine,(e.g., as may be mandatory for nuclear and power industries and thelike), x-ray testing of one or more parts of the industrial machine(e.g., turbine blades and the like), video analysis for detection ofvibration of one or more parts of the industrial machine, electronicfield testing of one or more parts of the industrial machine, magneticfield testing of one or more parts of the industrial machine, acousticdetection of one or more parts of the industrial machine, power and/orcurrent and/or voltage testing of one or more parts of the industrialmachine, (e.g., applying algorithms comparable to those used forvibration analysis to determine when current changes are anomalies),spectrum analysis of power consumed by a machine (e.g., a rotatingmachine and the like), correlation of mechanical and power faults of oneor more parts of the industrial machine, sound meter for validatingsound produced by or at least in proximity to one or more parts of theindustrial machine, and the like. In embodiments, machine learning maybe applied to any of these sources of testing data individually todetect patterns, and the like that may be useful in detecting when anoticeable change in, for example, a detected pattern has occurred or isabout to occur.

In embodiments, combinations of diagnostic testing, such as thosedescribed herein may be used by machine learning to validate orrepudiate one or more potential sources as producing anomalies that mayindicate a need for service and the like. In embodiments, combininginfrared thermography with motor testing for example, such as byapplying a test load onto the motor while capturing infrared images maybe useful in determining combinations of conditions may indicate apotential failure, or at least a condition associated with a failure, aneed for service, and the like. In embodiments, combining, for examplesounds meter capture with non-destructive testing may produce soundpatterns that may be compared to baseline sounds for the specificnon-destructive test condition; thereby allowing for multi-modalassessment of results (non-destructive testing results and sound testresults). In embodiments, variations in sound produced by or proximal toan industrial machine may indicate a potential failure conditions,validate a candidate failure condition, and/or diminish the likelihoodof a potential failure. In embodiments, combining multiple modes ofnon-destructive testing, such as acoustic and x-ray may help determineif a condition that may be detected in one of the testing modes (e.g.,acoustic) correlates to a potential anomaly detectable in the othertesting mode (e.g., x-ray) and the like. In embodiments, machinelearning may develop an array of test conditions, test results, anddegrees of compliance with expected results for each of thediagnostic/testing scenarios described herein, and the like. Such anarray may facilitate determining when anomalies represent validpotential failure conditions.

In embodiments, each test condition, such as those described aboveherein may be applied and results may be captured. While a given testcondition is being applied, each other test condition may be applied,thereby facilitating collection of combinations of each test conditionwith each other test condition. Results for each combination may becaptured and represented in an array, such as the array described above.Test condition combination testing may be performed when a service call,such as preventive maintenance or repair is required. In embodiments,the industrial machine predictive maintenance system may facilitatecoordinating maintenance, such as replacement of worn bearings in anindustrial machine. The test condition combination array may beconsulted to determine which test conditions might be applied incombination with post bearing replacement testing, such as be detectingone or more cells in the array along post bearing replacement testingaxis has little or no combination data. A work order and/or procedurefor post bearing replacement testing may be adapted, such asconditionally, and for specific instances, to include applying theadditional testing condition indicated by the specific cell in thearray. Such as approach may increase testing data, while distributingthe burden of testing across time, or at least across instances ofperforming service on the industrial machine.

In embodiments, machine learning may also be applied to combinationcondition testing, such as for detecting which combinations of testingconditions correlate best to actual failures. By learning whichcombinations correlate to failures, combinations that are less likely toyield a potential failure may be deprioritized so that valuable testingresources, such as service personnel and the like can be directed tocombination testing with a greater likelihood of yielding actionableinformation.

In embodiments, test results from a first mode of testing of a specificindustrial machine, such as motor testing may be processed with machinelearning algorithms and the like that may correlate certain machinetesting results with one or more candidate failure modes. Test resultsfrom a second mode of testing of the specific machine, such as torsionaltesting may be processed with the machine learning algorithms and thelike that may correlate certain torsional testing results with one ormore candidate failure modes. The one or more candidate failure modesfrom the machine testing may be compared with those of the torsionaltesting. Any candidate failure modes that match for the two types oftesting may be candidates for processing combined test results withmachine learning. When the machine testing results and the torsionaltesting results are combined and processed with machine learning,candidate failure modes may be correlated thereto. If one of thecandidate failure modes of the combined testing matches any candidatefailure modes of the combined testing, a likelihood of the combinedtesting indicating a likelihood of failure may be strengthened. Whensuch confirmation is detected through this combined testing resultmachine learning process, a service/repair action may be initiated toprevent failure of the specific industrial machine. In addition, testingprocedures may be adapted to include combination testing so that thelikely combined test result failure mode may be avoided in otherindustrial machines.

Referring to FIG. 318, an industrial machine predictive maintenancesystem 30202 may execute machine learning algorithms 30204 and the likeon data from a range of diagnostic testing systems, including withoutlimitation an infrared thermography system 30206, an ultrasonic testingsystem 30208, a motor testing system 30210, a current and voltagetesting system 30212, a torsional testing system 30214, anon-destructive testing system 30216, power, current and/or a voltagetesting system 30218, a sound testing system 30220, and the like. Theindustrial machine predictive maintenance system 30202 may access alibrary of testing results 30222 that may include test results for thesetesting systems for prior invocations of tests on a specific industrialmachine, and or on similar industrial machines. These results may beprocessed by the machine learning algorithms with failure modeinformation for the specific industrial machine and/or similarindustrial machines to determine test conditions, and in particularcombination of test conditions may correlate to specific failure modes.The machine learning algorithms 30204 may use artificial intelligencetechniques to determine patterns, similarities, and the like among datafrom the library, thereby facilitating detection of combinations oftesting conditions that may correlate to one or more failure modes.

In embodiments, a method of improving correlation between diagnostictest results and machine failures may include improving correlationbetween results of a plurality of diagnostic tests performed onindustrial machines and failure information for failures of similarindustrial machines by detecting at least one of patterns in thediagnostic test results that correlate to machine failures, similaritiesof diagnostic test results with machine failures. In embodiments, asingle type of machine failure correlates to failure results of a subsetof the diagnostic tests.

In embodiments, improved methods and systems for industrial machinemaintenance, including methods and systems that facilitate collecting,discovering, capturing, disseminating, managing, and processinginformation about industrial machines, including factual information(such as about internal structures, parts and components), operationalinformation and procedural information, including know-how and otherinformation relevant to maintenance, service and repairs may includemethods for rating a range of services and service providers associatedwith industrial machine predictive maintenance and the like. Inembodiments, service providers for performing maintenance and relatedactivities may be rated. While performing a service prescribed in aservice procedure, a service provider (e.g., a technician and the like)may be evaluated for the degree to which (s)he follows the procedure.The degree to which the procedure is followed may be captured implicitlyby independently determining if a step has been completed in the orderspecified. In embodiments, a procedure that requires removing a bearingcover panel followed by taking a photograph of the bearings may beverified by requiring the service technician to submit a photograph ofthe uncovered bearings before proceeding through the process. Inembodiments, the service technician may use a user interface of acomputing device, such as a tablet, portable phone, industrial portablecomputer and the like via which the technician accesses the serviceprocedure. The service technician may be rated along a range ofcriteria, including without limitation, ease of scheduling, degree ofexpertise/training with a specific machine and/or service activity, aresult of post-service diagnostic testing (e.g., self-testing and thelike), estimated versus actual costs for the service, promptness forperforming the service as scheduled, cleanliness however subjective thatcriteria may be, adherence to procedure (e.g., as described above andthe like) dependence on other resources, such as third-parties and thelike.

In embodiments, a vendor rating system 30300 is depicted in FIG. 319.The vendor rating system 30300 may include a vendor rating facility30302 that captures information about a vendor 30306 (e.g., location(s),user feedback, and the like), service data for one or more procedures30308 that the vendor 30306 alleges to know, vendor rating weightingdata 30310 that may impact how information is used to rate vendors(e.g., older data may be weighted less heavily than newer data, serviceon machines with very little service information may be weighted lessheavily, and the like). The vendor rating system facility 30302 mayfurther consider overall experience level of a vendor by applying anexperience scale 30312 that impact a confidence factor of a specificvendor rating based on the vendor's experience and extent of rating.Service technician input 30314 may be considered, such as structured(e.g., multiple choice responses) and/or freeform input that a servicetechnician may provide about a service activity and the like to explainwhy a procedure was not followed or why a service took longer thananticipated and the like. The vendor rating facility 30302 may furtherreceive information from the diagnostic testing 30222, such as testsperformed and results of tests associated with a service action that maybe used to evaluate success of the service action performed. Thediagnostic testing information 30222 may include information fromdiagnostics tests such as, infrared thermography, ultrasonic testing,motor testing, current/voltage testing, torsional testing,non-destructive testing, power density testing, sound testing and thelike. In embodiments, the vendor rating facility 30302 may rate vendorson a range of vendor rating criteria 30316 including, without limitationresults of post service diagnostics as may be determined from thediagnostics test results data 30222 and the like. Vendor rating criteriamay further include east of schedule, degree of experience with aprocedure, machine, and the like, cost, promptness, cleanliness,adherence to procedures, and the like. Vendor rating results may bestored and accessed in a vendor rating results data store 30322 that maybe processed with machine learning algorithms 30324 to improvecorrelation between, for example, a vendor rating criterion (e.g.,degree of experience) and a vendor's ratings.

In embodiments, a method of vendor rating may include determining arating for an industrial machine service provider by gathering feedbackabout industrial machine services provided by the service provider andcomparing the feedback to a plurality of rating criteria comprisingresults of diagnostics tests performed after completion of at least oneindustrial machine service, scheduling the service provider, cost of theservice provided, promptness of the service provider, cleanliness of theservice provider, adherence to a procedure for the at least oneindustrial machine service, a measure of experience of the serviceprovider with at least one of the procedure and the industrial machine.In embodiments, the method may include improving correlation of vendorrating results with rating criteria by applying machine learning tovendor rating results and incorporating an output of the machinelearning when rating a vendor.

In embodiments, improved methods and systems for industrial machinemaintenance, including methods and systems that facilitate collecting,discovering, capturing, disseminating, managing, and processinginformation about industrial machines, including factual information(such as about internal structures, parts and components), operationalinformation and procedural information, including know-how and otherinformation relevant to maintenance, service and repairs may includemethods for rating a range of activities and information associated withindustrial machine predictive maintenance and the like. In embodiments,procedural information for performing maintenance and related activitiesmay be rated. While performing a service prescribed in a serviceprocedure, a service provider (e.g., a technician and the like) mayindicate a rating for each procedure, such as for each substantiveservice procedure action, through a user interface via which thetechnician accesses the service procedure. The service technician mayrate each procedure along a range of criteria, including withoutlimitation, ease of access to the information, educational value of theinformation, accuracy of the descriptions, accuracy of the images,accuracy of the sequence, degree of difficulty to perform the service,and the like. Service providers and the like who rely on proceduralinformation for performing maintenance and the like on one or moremachines may develop know how regarding servicing systems using suchprocedural information. This know how may be captured in a procedurerating system through free form comments associated with the procedure,via suggested edits to the published procedures, and the like.

In embodiments, a procedure to perform a maintenance task may be clearto a service technician who is familiar with the particular machine, yetit may not be sufficiently clear to service personnel with lessexperience. Therefore, information about the service techniciancompleting the procedure rating task may be applied to better weight theratings. Additionally, a service procedure may be rated on an experiencescale that may facilitate identifying when a less experienced personcould be used to perform a service task and when an experienced provideris preferred. Such information may be useful to an industrial machinepredictive maintenance system for facilitating selection of a serviceentity suitable for performing a required service task and the like. Inembodiments, an industrial machine predictive maintenance system maygather information that may be descriptive of various aspects of aservice/maintenance procedure, such as the experience scale rating whenfacilitating access to vetted service personnel. In particular, if aservice procedure is rated as highly complex to follow, then serviceentities that have few or no experienced personnel available forperforming the service may by bypassed or at least may be presentedbelow service entities that have greater experience, greater numbers ofavailable experienced service technicians and the like. Ratingprocedural information may further enhance systems for generatingservice procedural information by identifying characteristics of serviceprocedure that are preferred over those that are found to be lacking andthe like.

In embodiments, such as shown in FIG. 320, methods and systems forrating industrial machine service and/or repair procedures may include aprocedure rating facility 30402 that may aggregate various sources ofprocedure rating content and produce one or more ratings for theprocedure, such as ease of use, accuracy, flexibility and the like. Sucha rating facility 30402 may have access to the procedure 30406, such asto process the text, images, flow charts and the like in the procedure;thereby facilitating rating various elements that contribute to theprocedure. The procedure rating facility 30402 may also have access toservice data 30408 for the procedure, such as a long of instance of useof the procedure, and the like. Such service data may be useful indetermining a degree of confidence of a rating of the procedure. Ratingfor procedures that are used less often may have lower confidence thanratings for often used procedures, due at least in part to the lack ofcomparative data for the lower-use procedures. Rating procedures mayalso include accessing weighting 30410 of factors that contribute to therating, such weighting may be explicitly stated, implicitly determined,and may vary based on factors such as age of the procedure, availabilityof materials required to follow the procedure, and the like. Inembodiments, rating some procedures may be impacted by experience ofcontributors to the rating process, such as service technicians,supervisors, procedure quality testers, and the like. Therefore, anexperience scale 30412 may be applied to the rating algorithm to, forexample, impact the aspects of a procedure that a contributor with givenexperience may be permitted to evaluate, and the like. In embodiments,service technician and other contributor inputs 30414 to the ratingprocess may be gathered explicitly, such as through a contributormarking a rating scale for various aspects of the procedure (e.g., thetext of the procedure, the translation of a procedure, and the like).Contributor input may be gathered implicitly, such as by tracking thetime that it takes to perform the steps in the procedure, and the like.In embodiments, if a service technician followed different steps oradditional steps than those presented in the procedure, the procedurerating facility may take this input and reasons for these other steps asinfluence of the rating of the procedure. This feedback may helpidentify procedures with inaccurate machine analysis and ormanufacturers guidance that may help in improving service quality.Improper machine fault diagnosis may be analyzed by artificialintelligence, such as the machine learning facility 30424 to improveanalysis. Feedback from technicians and procedure rating analysis andresults may be made available or pushed to the procedure developer(e.g., the industrial machine manufacturer and the like) to facilitateimproving the procedure to achieve better and faster repairs. Throughincentivized feedback programs and proper use thereof, such as for therating procedures 30402, institutional knowledge may permeate everyaspect of a preventive maintenance system without requiring one-on-onetraining like in the past.

In embodiments, a procedure rating facility, such as the rating facility30402 may further have access to rating criteria 30416, which mayinclude without limitation, ease of accessing the procedure, ease oftranslating the procedure, educational value of the procedure, accuracyof the text, accuracy of the images/graphics, accuracy of relatedcontent (e.g., parts lists), validity of the sequence of steps, degreeof difficulty overall to obtain an error free result from the procedurewhen using it for the first time, dependence on other steps that may ormay not be directly documented, and the like. A rating facility, such asthe procedure rating facility 30402 may produce procedure rating results30422 that may be stored electronically, such as in a non-volatilecomputer-accessible memory and the like. In embodiments, ratings forprocedures for a specific industrial machine may be stored in one ormore of the smart RFID components disposed with the machine. Theprocedure rating results 30422 may be improved through use of themachine learning 30424 that works cooperatively with the procedurerating facility 30402, and the like.

In embodiments, a method for rating an industrial maintenance proceduremay include determining a rating for an industrial machine serviceprocedure by gathering feedback about the procedure from serviceproviders who use the procedure to perform an industrial machine serviceand comparing the feedback to a plurality of rating criteria comprisingease of access of the procedure, ease of translation, educational value,accuracy of content, sequence accuracy, ease of following the procedure,and dependence on non-procedure actions. The method may further includeimproving correlation of procedure rating results with rating criteriaby applying machine learning to procedure rating results andincorporating an output of the machine learning when rating a procedure.

In embodiments, Blockchain™ techniques and applications, such asdecentralized voting, cryptographic hashing, verifiability, security,open access, speed of access and update, as well as ease of addingparticipants (e.g., contributors, verifiers and the like) may be appliedto the industrial machine predictive maintenance methods and systemsdescribed herein. Collection of data, such as operational, test,failure, and the like from industrial machines may be processed in aBlockchain™ approach that facilitates ensuring verifiability ofinformation regarding system status, failures, and the like.Transactions for parts orders, service orders, and the like may beprocessed in a Blockchain™ thereby increasing security and verifiabilityof transactions, including information such as costs, and the like thatmay be utilized by the predictive maintenance systems described hereinto manage industrial machine maintenance and service activities. Otheruses of block chain may include securing a distributed public ledger,such as the distributed ledger 29602 depicted in and described inassociation with FIG. 312 herein.

In embodiments, transactions conducted over a peer-to-peer network ofindustrial machines, such as IoT devices and the like may be operated asa Blockchain™ enabled distributed ledger, thereby reducing a dependencyon a centralized control or repository of industrial machine and thelike preventive maintenance data. In an example of Blockchain™functionality in an industrial machine predictive maintenance system,changes to smart RFID elements on individual machines and theircounterpart network-resident copy may be processed through a Blockchain™distributed ledger system that facilitates open access to information inthe RFID, such as by accessing the relevant information in thenetwork-resident copy.

In embodiments, FIG. 321 depicts a Blockchain™ for transactionsassociated with a specific industrial machine 30500 that may beinitiated 30502 when the industrial machine is shipped or finalized forshipment. As further transactions of the specific industrial machine areperformed, such as during an installation 30504, collecting operationalinformation from sensors deployed with the industrial machine 30506,service events of the machine 30508, parts and service orders 30510,diagnostic activity 30512, and the like each may be added to theBlockchain™ for the specific industrial machine; thereby providing asecure, verifiable, traceable data set for the industrial machine thatcan be leveraged by the predictive maintenance methods and systemsdescribed herein.

In embodiments, a method of accumulating information about an industrialmachine may include initiating a blockchain of industrial machineinformation for a specific industrial machine by generating aninitiating block, and generating subsequent blocks of the specificindustrial machine blockchain by combining data from at least one ofshipment readiness, installation, operational sensor data, serviceevents, parts orders, service orders, and diagnostic activity and a hashof the most recently generated block in the blockchain.

In embodiments, predictive maintenance schedules, actions, and the likemay be based on analysis of industrial machine operational data, such asdata from sensors deployed with the industrial machine. Determining amaintenance triggering threshold for operational data, including senseddata, may include identifying a type of effect the data represents andthen determining data values that represent acceptable operation,questionable operation, unacceptable operation, and other types ofoperation. In embodiments, vibration sensors deployed to detect andmonitor vibration activity of industrial machine components, structuralelements, and the like may facilitate determining how vibration ofmachine parts contributes to predictive maintenance actions. Determininga severity of vibration data from the sensors relative to timing and thelike of predictive maintenance actions may require more thanconventional vibration analysis. In embodiments, vibration measures maybe translated into severity units that may be used when predictingmaintenance requirements and the like.

In embodiments, while vibration may be useful for determining negativeeffects on industrial machines, vibration analysis is generally complexand varies greatly based on frequency of vibration, vibration source,material being vibrated, machine operating cycles per minute, and thelike. A measure of vibration, such as vibration velocity may be usefulfor determining when vibration is a problem for a mid-range vibrationfrequency, but alone it fails to usefully provide insight at low andhigh frequencies. Therefore, vibration analysis that is frequencyindependent, such as vibration analysis measures that are normalized,may result in useful predictive maintenance information.

In embodiments, normalizing vibration analysis results into severityunits as described herein may facilitate vibration frequencyindependence. Overall vibration spectra, RMS levels, and the like may beexpressed in units of displacement, velocity, acceleration and the like.In an example, bearing cap vibration readings may be expressed asvibration velocity at least because it directly relates to mechanicalseverity of the vibration. As noted above, while vibration velocity maybe sufficient for mid-range frequency components, low and high frequencycomponents exhibit significant exceptions to the relevance of vibrationvelocity for predictive maintenance algorithms. It will be appreciatedin light of the application that vibration velocity man be characterizedthrough amplitude-versus-frequency charting and the like that, ineffect, linearly lower the velocity severity requirements (e.g.,vibration amplitude and the like) for low and high frequencies, such aswhen compared to mid-range frequency velocity severity requirements.

In embodiments, the methods and systems described herein extend andenhance methodologies of frequency charting to facilitate normalizingvibration spectra so that it can be expressed as vibration severityunits that are consistent across wide vibration frequency spectra, suchas from near-zero frequency to well over 18,000 cycles per minute (cpm).Components of the vibration spectra that occur at frequencies below alow-end linearity frequency (e.g., a low-end knee frequency value) willbe processed with an algorithm that normalizes to a value ofdisplacement (e.g., a preset value of millimeters of displacement)because displacement (e.g., amplitude) has been shown to be a moresignificant indicator of severity than velocity at lower frequencies.Components of vibration spectra that occur at frequencies above ahigh-end linearity frequency (e.g., a high-end knee frequency value)will be processed with an algorithm that normalizes to a value of unitsof gravity (e.g., a preset value of g's or g force). The net result isthat each range of the frequency spectra (below the low-end kneethreshold, mid-range, and above the high-end knee threshold) can bemapped uniformly to severity units. In many examples, the frequencyspectra may be broken into three ranges (below low-end knee threshold,mid-range, and above high-end knee threshold), fewer or more ranges offrequency spectra may be determined and applied without exceeding thescope of the vibration data normalization techniques for generatingpredictive maintenance vibration severity units.

In embodiments, methods and systems include normalizing vibrationamplitude units into units that are independent of frequency. Theseunits can be referred to as severity units or action units. In manyexamples, vibration spectra, overall levels or root-mean-square levelsare expressed in units of displacement, velocity or acceleration. Forbearing cap readings, for example, vibration velocity is most commonlyused as it may be directly related to mechanical severity. Althoughsufficient for mid-frequency components, there can be, however,significant exceptions for low frequency and high frequency domains. Itwill be appreciated in light of the disclosure that manyamplitude-versus-frequency severity charts have been constructed tolinearly lower the velocity severity requirement for both the lower andthe higher frequency components depicted in the chart.

In embodiments, the methods and systems include development andconstruction of a severity graph to normalize vibration spectra asseverity units. By way of this example, lower frequency components belowa predetermined knee level of about 1,200 cycles per minute, as depictedin FIG. 322, will be gained by a predetermined factor (as a function ofthe slope) such that its amplitude in severity units may be normalizedwith respect to severity. Similarly, for higher frequency componentsabove a knee level of about 18,000 cycles per minutes, spectral peaksare also gained by a different predetermined factor to achieve severityflatness. In embodiments, spectra displayed in severity units may bedisplayed with horizontal lines to demarcate severity. In many aspectsof the embodiments, other spectral components related to one or morebearing defect frequencies and/or one or more bearing resonancefrequencies may have their corresponding amplitudes adjusted forseverity. By way of this example, other spectral components related toone or more bearing defect frequencies may have their correspondingamplitudes increased to adjust for severity, other spectral componentsrelated to one or more bearing resonance frequencies may have theircorresponding amplitudes decreased to adjust for severity. In addition,other digital processing techniques, which output spectra such asenveloping, may be employed to supplement or superimpose spectral peakswithin the severity spectrum. In embodiments, the final resultingseverity spectrum may then be displayed local, remotely and/or accessedthrough a cloud network facility for presentation and analyticalpurposes. In embodiments, the final resulting severity spectrum may befed to an expert system for analysis and evaluation of the severity. Inmany aspects of the embodiments, an overall level may be calculated orderived from this “normalized” spectrum to produce an overall level or aroot-mean-square level in units of severity rather than the moretypically collection of disparate units currently utilized by vibrationmonitoring systems.

In embodiments, FIG. 322 depicts a diagram showing a severity unitconversion function for normalizing vibration sensor data for casingvibration on industrial machinery. The severity unit conversion function30602 includes vibration displacement rate (inches per second) along avertical axis 30604 and vibration frequency cpm (cycles per minute)along a horizontal axis 30606. A low-end frequency demarcation 30608 isset at 1200 cpm, defining the upper end of the low-end vibrationfrequency region 30610 as well as the lower end of the mid-frequencyregion 30612. A high-end frequency demarcation 30614 is set at 18000cpm, defining a lower end of the high-end vibration frequency region30616 as well as the high-end of the mid-frequency region 30612.

Severity for the embodiment of FIG. 322 is calculated as follows:

S=M×A  (30601)

In the equation 30601, S is the severity value being calculated, A is amid-range severity limit, and M is a severity normalizing value that iscalculated for each of the three vibration spectra ranges as follows:

for the low-end range 30610: M=vibration frequency/low-end demarcationvalue;

for the mid-range 30612: M=1; and

for the high-end range 30616: M=high-end demarcation value/vibrationfrequency.

In the example of the embodiments of FIG. 322, the low-end rangeM=frequency/1200 and for the high-end range M=18000/frequency. Thisresults in an acceptable severity value of approximately 2.5 mils forthe low-end range and 2.5 g's for the high-end range.

In embodiments, the severity normalization function exemplified in FIG.322 can facilitate developing severity units for each frequency rangethat may be used by the predictive maintenance methods and systemsdescribed herein.

In embodiments, five severity units are identified and may be applied toeach frequency range. Severity units may be named: acceptable, watch,resurvey, action soon, immediate, and the like. In embodiments,vibration data that results in an acceptable severity unit has little,if any, impact on predictive maintenance analysis and actionrecommendations. Vibration sensor data studies that result in acceptableseverity unit analysis may be gathered and further analyzed forvariations among industrial machines, such as similar industrialmachines, similar portions of industrial machines, different generationsof industrial machine or portion thereof and the like.

In embodiments, additional severity categories may be added as depictedin FIG. 323. With continuing reference to FIG. 323, the exemplaryseverity chart may define severity levels with associated actions forthose levels. By way of this example, the severity chart may beassociated with spectral peaks taken with a bearing cap mountedaccelerometer. The range at which the one or more detected signals aredeemed acceptable and, therefore, the least severe across the threeranges of the detected signal are less than about 2.5 thousandths of aninch peak-to-peak (about 63.5 micrometers peak-to-peak) when measuringdisplacement for a regime that is less than about 1,200 cycles perminute or less than about 20 Hz. For the regime that is about 1,200cycles per minute to about 18,000 cycles per minute or about 20 Hz toabout 300 Hz, the severity chart may assess signals in terms of velocityand the acceptable and, therefore, least severe level is less than about0.15 inches per second at peak (about 3.81 millimeters per second atpeak). For the regime that is greater than about 18,500 cycles perminute or greater than about 300 Hz, the severity chart may assesssignals in terms of acceleration and the acceptable and, therefore,least severe level is less than about 2.5 g level at peak.

The range at which the one or more detected signals are deemed worthy ofwatching and, therefore, one level higher than the least severe acrossthe three ranges of the detected signal are between 2.5 thousandths ofan inch peak-to-peak (about 63.5 micrometers peak-to-peak) and 5thousandths of an inch peak-to-peak (about 127 micrometers peak-to-peak)when measuring displacement for a regime that is less than about 1,200cycles per minute or less than about 20 Hz. For the regime that is about1,200 cycles per minute to about 18,000 cycles per minute or about 20 Hzto about 300 Hz, the severity chart may assess signals in terms ofvelocity and the worth to watch and, therefore, one level higher thanthe least severe level is between about 0.15 inches per second at peak(about 33.8 millimeters per second at peak) and about 0.3 inches persecond at peak (about 67.6 millimeters per second at peak). For theregime that is greater than about 18,500 cycles per minute or greaterthan about 300 Hz, the severity chart may assess signals in terms ofacceleration and the worthy to watch and, therefore, one level up fromthe least severe level is between about a 2.5 g level at peak and abouta 5 g level at peak.

The range at which the one or more detected signals are determined to besufficient to suggest or require a re-survey of the machine or routefrom which the one or more signals were obtained and, therefore, onelevel higher in severity than the watch level and two levels of severityhigher than the least severe across the three ranges of the detectedsignal are between 2.5 thousandths of an inch peak-to-peak (about 63.5micrometers peak-to-peak) and 5 thousandths of an inch peak-to-peak(about 127 micrometers peak-to-peak) when measuring displacement for aregime that is less than about 1,200 cycles per minute or less thanabout 20 Hz. For the regime that is about 1,200 cycles per minute toabout 18,000 cycles per minute or about 20 Hz to about 300 Hz, theseverity chart may assess signals in terms of velocity and define arange in which it may be sufficient to suggest or require a re-survey ofthe machine or route from which the one or more signals were obtainedbetween about 0.3 inches per second at peak (about 7.62 millimeters persecond at peak) and about 0.6 inches per second at peak (about 15.24millimeters per second at peak). For the regime that is greater thanabout 18,500 cycles per minute or greater than about 300 Hz, theseverity chart may assess signals in terms of acceleration and besufficient to suggest or require a re-survey of the machine or routefrom which the one or more signals were obtained between about a 5 glevel at peak and about a 10 g level at peak.

By way of this example, the range at which the one or more detectedsignals are determined to be sufficient to flag for action soon and,therefore, one level below a severity level to flag for action. In otherexamples, there can be a flag for action now and a flag action includinga flag for shutdown when the severity of one or more detected signalswarrant such a flag. When measuring displacement for a regime that isless than about 1,200 cycles per minute or less than about 20 Hz, thesufficient to flag for action soon range may be between about 10thousandths of an inch peak-to-peak (about 254 micrometers peak-to-peak)and about 16.6 thousandths of an inch peak-to-peak (about 421.64micrometers peak-to-peak) For the regime that is about 1,200 cycles perminute to about 18,000 cycles per minute or about 20 Hz to about 300 Hz,the severity chart may assess signals in terms of velocity and define arange in which it may be sufficient to suggest or require a re-survey ofthe machine or route from which the one or more signals were obtainedbetween about 0.6 inch per second at peak (about 15.24 millimeters persecond at peak) and about 1 inch per second at peak (about 25.4millimeters per second at peak). For the regime that is greater thanabout 18,500 cycles per minute or greater than about 300 Hz, theseverity chart may assess signals in terms of acceleration and besufficient to suggest or require a re-survey of the machine or routefrom which the one or more signals were obtained between about a 10 glevel at peak and about a 16.6 g level at peak.

By way of this example, the range at which the one or more detectedsignals are determined to be sufficient to flag for immediate actionand, therefore, at the highest severity level. In other examples, therecan be a flag for immediate action and a flag action including a flagfor shutdown when the severity of one or more detected signals warrantsuch a flag. When measuring displacement for a regime that is less thanabout 1,200 cycles per minute or less than about 20 Hz, the sufficientto flag for immediate action soon range may be above about 16.6thousandths of an inch peak-to-peak (about 421.64 micrometerspeak-to-peak) For the regime that is about 1,200 cycles per minute toabout 18,000 cycles per minute or about 20 Hz to about 300 Hz, theseverity chart may assess signals in terms of velocity and define arange in which it may be sufficient to flag for immediate action aboveabout 1 inch per second at peak (about 25.4 millimeters per second atpeak). For the regime that is greater than about 18,500 cycles perminute or greater than about 300 Hz, the severity chart may assesssignals in terms of acceleration and be sufficient to flag for immediateaction soon above about a 16.6 g level at peak.

It will be appreciated in light of the disclosure that the severitychart in FIG. 323 depicts 0.15 inch per second velocity at 1,250 cyclesper second in the Acceptable category. The conversion betweendisplacement, velocity and acceleration depicted in FIG. 1 shows that2.5 thousandths of an inch displacement peak-to-peak is equivalent to0.15 inches per second velocity at 1,250 cycles per second in thenormalization to determine severity units. FIG. 1 also shows that 0.2inches per second velocity at peak at 61,450 cycles per minute isequivalent 2.5 g level of acceleration. The Watch category spans 6 dB.The Resurvey category spans 6 dB and the Action Soon category spansabout 4.5 dB.

It will be appreciated in light of the disclosure that many examples ofseverity charts may be based on highly specific equipment types. In manyexamples, some of these classifications may be simplified because manycategories of machines that run at sufficiently low or relatively slowerspeeds may not need separate severity categories. In these examples,severity units based on velocity may be sufficient to provide one ordiagnoses. In many examples, communication between different subsystemssuch as a raw data server that may serve up vibration waveform, spectrumand overall levels and an expert system engine that must translate thisraw data into meaningful severity units may be significantly simplifiedby the use of normalizations to produce the severity units.

In embodiments, the severity units may be applied to non-vibration datawhere signal processing techniques may be applied to any raw set of datathat has specialized significance, but which must be normalized to besuccessfully compared or analyzed. In embodiments, actuarial dataregarding the viability of a specific pharmaceutical treatment that maybe gender specific may be normalized to the general population. It willbe appreciated in light of the disclosure that one or more establishedtechniques or guidelines normalizing the gender-specific data to agender-less universe becomes useful for subsystem communication to AI,statistical, tutorial or other relevant systems.

In embodiments, vibration data that results in a watch severity unit mayimpact aspects of predictive maintenance recommendations, such as afrequency of occurrences of vibration data collection and analysis.Watch severity unit determination may result in conducting at leastvibration data collection and analysis more frequently. It may alsoresult in checking other conditions of the components being vibrated,such as by performing calibration, diagnostic testing, visual inspectionand the like.

In embodiments, vibration data that results in a resurvey severity unitmay trigger performing vibration data collection and analysis as soon aspossible. Resurvey severity unit determination may result in a signal(e.g., a set of commands and the like) being transmitted to relevantportions of the affected industrial machine to configure the datacollection and routing functionality and elements to repeat thevibration data collection and analysis again. It may also result inconfiguring the industrial machine data collection control systems toinitiate data collection from other sensors for the involved industrialmachine elements. Likewise, it could raise the priority of collectingcomparable vibration sensor data from other similar industrial machinesso that it can be available for comparative analysis of the resurveyedvibration study and the like.

In embodiments, vibration data that results in an action soon severityunit may trigger scheduling a service action of the affected parts wellahead of a next scheduled maintenance for a portion of the industrialmachine with the affected parts. It may also result in escalatingactions (e.g., preventive, survey, analysis, and the like) for relatedelements. In an example, if vibration data for a motor indicates takingaction soon, vibration data collection, preventive maintenance actions,calibration actions and the like may be activated for a drive shaft ofthe motor, a gearbox being driven by the driveshaft, and the like.

In embodiments, vibration data that results in an immediate severityunit may be treated as constructive approval to perform all necessarypart replacement as soon as possible, thereby triggering ordering ofreplacement parts, materials, and the like to perform one or moreservice actions on the industrial machine. Such a result may alsotrigger certain automatic actions such as stopping use of the industrialmachine, reducing the duty cycle of the industrial machine, reducing anoperating cycle rate of the industrial machine, and the like untilservice is performed, and the like.

An embodiment of severity units applied to vibration across a widevibration frequency range is representatively depicted in FIG. 323. Inthe representative embodiment of FIG. 323, each of five severity unitsare mapped to the three vibration spectra regions represented in FIG.322, specifically for vibration frequencies below 1200 cpm, between 1200cpm and 18000 cpm, and above 18000 cpm.

In embodiments, within each spectral region severity units are defined.For the spectral region below the low-end threshold (e.g., 1200 cpm),vibration displacement below 2.5 mils peak-to-peak meets the acceptableseverity unit criteria; between 2.5 and 50 indicates a watch severityunit; between 5.0 and 10.0 indicates a resurvey severity unity; between10.0 and 16.6 mils displacement indicates an action soon severity unit,and displacement greater than 16.6 mils triggers an immediate actionseverity unit. For vibration frequency spectra between 1200 cpm and18000 cpm, normal severity is characterized by displacement below 0.15inches per second peak (ipsp); watch is between 0.15 and 0.3 ipsp;resurvey is between 0.3 and 0.6 ipsp; action soon severity occursbetween 0.6 and 1.0 ipsp; and immediate action severity occurs forvibration displacement rates greater than 1.0 ipsp. For vibrationfrequency spectra greater than 18000 cpm, acceptable severity isindicated by vibration analysis indicating less than 2.5 gs peak; watchis indicated by 2.5 gs to 5.0 gs; resurvey for 5.0 gs to 10.0 gs; actionsoon for 10.0 gs to 16.6 gs; and immediate action severity unit isindicated for vibration that results in forces greater than 16.6 gs.

Applications of the severity unit methods and systems described hereininclude uses across a range of machines operating at various speeds.Unlike existing vibration analytical tools, the algorithm-based approachdescribed herein can readily handle slower speed machines by effectivelyremoving some unnecessary computational complexity associated with animpact of machine speed, and the like. In environments where differentmachines perform different actions, such as raw data analysis andseverity detection, communication bandwidth must be increased to supportproviding enough information to ensure robust severity determination.Use of the severity unit methods and systems described hereinsignificantly simplify data communication needs in such embodiments;thereby reducing communication bandwidth demand in correspondingenvironments and the like.

While this discussion of severity units is directed at vibration dataanalysis and the like, the methods and systems for severity unitdetermination and detection may be applied to data sources other thanvibration that can benefit from normalization for successful comparison.In embodiments, actuarial data regarding the viability of a specificpharmaceutical treatment for one or both genders may be normalized usingthe methods and systems described herein to be applied to the generalpopulation. Algorithms may be generated that accommodate existingguidelines for severity, yet extend them using the methods and systemsdescribed herein to produce gender-less (gender normalized) severitymeasures.

In embodiments, a method of predicting a service event from vibrationdata may include a set of operational steps including capturingvibration data from at least one vibration sensor disposed to capturevibration of a portion of an industrial machine. The captured vibrationdata may be processed to determine at least one of a frequency,amplitude, and gravitational force of the captured vibration. Next, asegment of a multi-segment vibration frequency spectra that bounds thecaptured vibration may be determined, based on for example thedetermined frequency. Thus, calculating a vibration severity unit forthe captured vibration may be based on the determined segment and atleast one of the peak amplitude and the gravitational force derived fromthe vibration data. Additionally, the method may include generating asignal in a predictive maintenance circuit for executing a maintenanceaction on the portion of the industrial machine based on the severityunit.

In embodiments, the segment is determined based on comparing thedetermined frequency to an upper limit and a lower limit of amid-segment of the multi-segment vibration frequency spectra. A firstsegment of the multi-segment vibration frequency spectra may includedetermined frequency values below a lower limit of a mid-segment of themulti-segment vibration frequency spectra. The lower limit of themid-segment of the multi-segment vibration frequency spectra may be 1200kHz and the upper limit may be 18000 kHz. In embodiments, a secondsegment of the multi-segment vibration frequency spectra may includedetermined frequency values above an upper limit of a mid-segment of themulti-segment vibration frequency spectra.

In embodiments, calculating a vibration severity unit may includeproducing a severity value by multiplying one of a plurality of severitynormalizing parameters by a mid-range severity limit and mapping thevibration severity value to one of a plurality of severity unit rangesof the determined segment. A first severity normalizing value of theplurality of normalizing values is calculated by dividing the determinedfrequency by a low-end frequency value of the mid-segment of themulti-segment vibration frequency spectra. A specific one of theplurality of severity normalizing parameters includes the first severitynormalizing value when the determined frequency value is less than thelow-end frequency value.

In embodiments, a second severity normalizing value of the plurality ofnormalizing values is calculated by dividing a high-end frequency valueof the mid-segment of the multi-segment vibration frequency spectra bythe determined frequency. A specific one of the plurality of severitynormalizing parameters includes the second severity normalizing valuewhen the determined frequency values is greater than the high-endfrequency value.

Regarding segments of the multi-segment vibration frequency spectra, afirst segment of the multi-segment vibration frequency spectra isdivided into a plurality of severity units based on the determinedamplitude of vibration. A second segment of the multi-segment vibrationfrequency spectra is divided into a plurality of severity units based onthe determined gravitational force.

In embodiments, the vibration severity unit is determined based on apeak displacement of the determined amplitude of vibration fordetermined vibration frequencies within the first segment of themulti-segment vibration frequency spectra. In an example, the vibrationseverity unit is determined based on the determined vibration-inducedgravitational force for determined vibration frequencies within thesecond segment of the multi-segment vibration frequency spectra.

In embodiments, the portion of the industrial machine may be a movingpart, a structural member supporting a moving part, a motor, a driveshaft, and the like.

In embodiments, a system for predicting a service event from vibrationdata may include an industrial machine that includes at least onevibration sensor disposed to capture vibration of a portion of theindustrial machine. The system may further include a vibration analysiscircuit in communication with the at least one vibration sensor and thatgenerates at least one of a frequency, peak amplitude, and gravitationalforce of the captured vibration. The system may yet further include amulti-segment vibration frequency spectra structure that facilitatesmapping the captured vibration to one vibration frequency segment of themultiple segments of vibration frequency. Also, the system may include aseverity unit algorithm that receives the determined frequency of thevibration and the corresponding mapped segment and produces a severityvalue which is then mapped to one of a plurality of severity unitsdefined for the corresponding mapped segment. In embodiments, the systemmay also include a signal generating circuit that receives the one ofthe plurality of severity units, and based thereon, signals a predictivemaintenance server to execute a corresponding maintenance action on theportion of the industrial machine.

In embodiments, vibration-related data collected from sensors disposedwith an industrial machine may include displacement, velocity,acceleration, and the like. Additionally, data such as velocity,acceleration and the like may be calculated from raw collected data,such as displacement gathered over known units of time and the like.Velocity may be based on a count of detectable vibration events in aspecific period. Velocity may be independent of a size or length of adisplacement occurrence. In embodiments, acceleration may be calculatedas a rate of change of velocity measures. In embodiments, accelerationmay be generated from one or more acceleration sensors that may detect atime of a start of displacement and relative time of an end ofdisplacement in a specific direction and based thereon may identify anacceleration of the part during a vibration occurrence. Vibration datamay be helpful in determining if a part may be subject to excessivevibration. Analyzing such vibration data to make the determinationinvolves factoring in aspects of vibration, such as frequency and thelike. As described herein, conventional approaches to vibration analysisfor determining a degree to which detected vibration may beunacceptable, requires evaluating vibration in different portions of thevibration spectra differently. A novel approach to normalize evaluationof an impact of vibration across an extended range of vibration spectra,such as a threshold of vibration beyond which the vibration is likely tocause a problem, such as a breakdown of the vibrating component maybenefit predictive maintenance systems, such as expert systems and thelike that may attempt to provide actionable information to machine ownerand the like.

In embodiments, Severity Units may facilitate normalizing vibrationanalysis for the purposes of determining if detected vibration isunacceptable by eliminating, or at least obfuscating the need forcalculating multiple vibration measures across a range of vibrationspectra. By normalizing different units of vibration measure overspectral ranges, Severity Units, also referred to herein as ActionUnits, may facilitate application of Severity Units for a wide range ofvibration analysis applications, including without limitation,industrial machine vibration analysis, moving part vibration analysis,complex mechanical system vibration and the like.

In embodiments, FIG. 324 depicts a vibration severity graph that chartsvibration frequency along the horizontal axis. The graph includes twovertical axes—one that represents traditional vibration measures thatare frequency dependent; the other represents Severity Units that areindependent of frequency. The traditional vibration measures a line30802 shows three segments, indicating safe vibration limits for threeranges of frequency. A severity units line 30804 shows a singlehorizontal line indicating a safe vibration-severity limit for allranges of frequency. For traditional vibration analysis derivatives ofvibration are adjusted for frequency. Such derivatives below the line30802 may represent acceptable levels of vibration. Similarly, vibrationderivatives above the 30802 may represent unacceptable levels ofvibration. However, the function required to determine whether a sampleof vibration results in a derivative above or below the line 30802 isdifferent for different vibration frequencies. The knee values 30806 and30808 may typically, as described herein align with vibrationfrequencies of 1200 CPM and 18000 CPM; however, material type, vibrationobject type and other factors may further impact the function toperform. In contrast, the methods and systems described herein forgenerating and using Severity Units and/or Action Units may be adaptedto generate a normalized limit for vibration severity a represented bythe line 30804. Severity/Action unit-based calculated measures ofvibration below the line 30804 may indicate safe vibration limits;whereas severity/action unit-based measures above the line 30804 mayrepresent unacceptable levels of vibration. An expert system, such as asystem for predicting maintenance events for industrial machines mayapply severity/action unit values for industrial machines in a simplecomparison function that compares a severity/action unit value to theseverity/action unit threshold value. When the unit value is below thethreshold value, an impact on a prediction of a need for maintenance maybe small or negligible. When the unit value is above the thresholdvalue, an impact on a prediction of a need for maintenance may besubstantive and may directly trigger predicting a maintenance event.Alternatively, the result of the comparison of a unit value with athreshold value may be used to adjust a weighting of other factors beingprocessed to predict a maintenance event. Through severity/action unitweighting of other factors, predicting maintenance needs for industrialmachines may combine below threshold or marginal results for vibrationand other factors into a prediction of industrial machine maintenance.

In embodiments, severity units may be calculated using other signalprocessing techniques. These other signal processing techniques mayproduce an Action Unit normalized representation of the sensed vibrationdata. In embodiments, other frequency thresholds may be used withvarious techniques and may be dependent on various factors of themachine part(s) being vibrated, such as without limitation severity peakvibration levels, gas pulse frequency peak levels, machinery componenttype, bearing fault frequencies and the like. In embodiments, normalizedseverity/action units may be weighted based on a component type forapplications, such as hammer mills, crushers, large horse power primemovers, soft-foundation (e.g., spring isolated) and the like. While theexample of FIG. 324 and others in this specification use a low thresholdof 1200 Hz and a high threshold of 18000 Hz, other values can be used,such as a low threshold of 500 Hz and a high threshold of 5000 Hz andthe like. The relationship between a low threshold and a high thresholdfor a given application may be based on a material, operating frequency,severity sensitivity, and the like.

Vibration events that may be detected through envelop processing and thelike, such as for roller bearing defects that cause machine cycledependent vibration events (e.g., a jolt as the roller bearing impactsthe defect). Once vibration events detected through envelop processingare captured, they can be processed to result in a peak value that canbe mapped to a severity unit frequency spectra. In this way,envelope-detected vibration events that may be filtered out through RMSor similar time-averaging calculations, can be mapped onto aSeverity/Action Unit frequency chart.

In embodiments, severity for various components in an industrial machineor portion thereof (e.g., a gear box and the like) may be combined intoan overall severity for the machine/portion. One approach is to generatean aggregated severity value by summing all the severity unitcalculations for one or more components in the machine/portion. Anotherapproach is to calculate an overall average severity for amachine/portion, such as by determining an average of the generatedseverity values. Other approaches for calculating an overall severityfor a machine/portion may include weighting a portion of the individualcomponent's severity value, and the like.

In embodiments, calculations of severity units for industrial machinecomponents, such as moving parts in an industrial machine (e.g., gears,shafts, motors, too heads, and the like) may be mapped onto a severitygraph as depicted in FIG. 323 and described herein, such as byidentifying in the map a correspondence between a spectral peak leveland a measure of severity level. A mapped severity level may bedetermined based on the identification. Graphical elements may beassigned to each severity level so that a severity of an industrialmachine component may be presented pictorially as, for example, anoverlay of an image, drawing, or other representation that showsindividual components in an industrial machine. FIG. 325 depicts a blockdiagram representing components 30902 of an industrial machine 30900with severity unit levels indicated by a graphical overlay elements30904. In embodiments, the overlay image of FIG. 309 may be presented ina graphical user interface that may facilitate data discovery by a userwho interacts with the overlay by, for example touching or otherwiseselecting one of the graphical overlay elements 30904. Such a scenariois depicted in FIG. 325. Component severity and related information inpop-up window 30908 is visualized in response to a user selecting thegraphical overlay element 30904. In embodiments, the graphical overlayelements 30904 may represent composite severity levels for a group ofcomponents, such as a gear box, motor assembly and the like. When acomposite graphical overlay element is selected, a second image, such asa detail of a gear box and the like may be visualized in the graphicaluser interface so that the user can dive into further details for thecomponents in the assembly, and the like.

In embodiments, severity units may be presented in context of a MasterAction Unit Nomogram (MAUN). In embodiments, vibration data may becollected for at least three dimensions; therefore, a 3-D MAUN thatpresents vibration data in action or severity units in a 3-Dpresentation may be produced.

In embodiments, raw vibration data may be provided to a predictivemaintenance system, such as a system that applies techniques such asmachine learning and the like to determine threshold for acceptablevibration across a range of spectra. However, learning from this rawinformation may require information about the environment and vibrationanalysis engineering that results in a highly complicated maintenanceprediction operation. Severity Units, such as those described herein,including MAUN and the like, may be provided to the predictivemaintenance system to simplify learning by more efficiently matching rawvibration data with normalized measures of vibration severity (e.g.,Severity Units and the like). Use of Severity Units and the like mayfurther reduce filtering and evaluation complexity for predictivemaintenance systems since at least some portion of these operations maybe incorporated into the generation of Severity Unit measures from theraw vibration data.

In embodiments, learning from such systems may be applied to SeverityUnit calculation functions, such as may be performed locally by a datacollection agent, local network processor, and the like as feedback.This feedback may be applied to threshold refinement algorithms thatadjust, for example, severity level (e.g., threshold) determination fromraw vibration data, so that vibration thresholds can be tuned for localconditions, and the like. Such feedback may further be useful inprocesses that attempt to determine which of a plurality of dataprocessing techniques/algorithms (e.g., to produce Severity and/orAction Units and the like) may produce more accurate MAUN measures.Doing so may reduce processing complexity and reduce data storagedemand, which may be desirable for reducing overall cost andsophistication of data collection devices and the like that may produceSeverity Unit data.

In embodiments, predictive maintenance methods and systems may beapplied to industrial machines, such as rotating equipment machines.Exemplary rotating equipment machines for which methods and systems ofpredictive maintenance described herein can be used may include, withoutlimitation drills, boring heads, polishers, motors, turbines, gearboxes, transmissions, rotary-vibratory adapters, drive shafts, computernumerical controlled (CNC) routers, lathes, mills, grinders,centrifuges, combustion engines, compressors, reciprocating engines,pumps, fans, blowers, generators, and the like. Manufacturers ofexemplary rotating equipment and related parties, such as testingservices, component manufacturers, sub-contractors, and the like mayhave access to technical data about such equipment on amachine-by-machine basis. Additionally, information that may beavailable about machines, sub-assemblies, individual components,accessories, rotating integrated parts, and the like may include designparameters, test specifications, operating specifications, revisions tothe products, and the like. This and related information may apply toone or more deployed machines, such as to a specific serial number, aproduct line of industrial machines, a given production version, aproduction run, and the like. Machine information available may coveraspects of the equipment that relate to one or more rotating components,such as a count of gear teeth of one or more gears (e.g., a gear boxsuch as a helical gearbox, worm reduction gearbox, planetary gearbox andthe like, a power transfer gear set, and the like), a count of motorrotor bars (e.g., rotor bars in a squirrel-cage rotor and winding, suchas a synchronous motor, and the like), RPM rate for rotating componentsand the like. Additionally, information may be available and utilizedfor predictive maintenance event planning and execution of industrialmachines, such as roller bearing-based systems including, withoutlimitation (count of roller balls, count of balls, count ofballs/roller, ball-to-roller contact angle(s), race dimension (e.g.,inner and outer race dimensions), count of vanes, count of flutes, modeshape (e.g., relative displacement and the like) data.

Providing access to rotating equipment information, such as thatexemplarily described herein, for predictive maintenance processing,such as with a predictive maintenance analysis circuit, may be automatedthrough a range of means including, without limitation; (i) storing datathat contains information about a portion of a rotating equipmentmachine in a non-volatile storage element integrated with or into themachine, or portion thereof, prior to deployment in the field; (ii)updating a non-volatile storage element integrated with or into themachine with the relevant rotating component information after or aspart of deployment, such as during a deployment validation operation andthe like; (iii) storing data representative of the rotating equipmentspecifications, measurements, production testing, and the like in anetwork accessible data storage facility (e.g., a cloud-based datastorage facility indexed by at least one of part, sub-system, machine orthe like identifier, such as a serial number or set thereof thatassociates a part (e.g., a roller bearing assembly) with amachine/deployment; (iv) a combination of (i) or (ii) and (iii), with atleast a subset of information stored in the non-volatile data storagefacility deployed with the machine (e.g., a serial number of themachine, serial number(s) of rotating equipment components, and thelike) that can be used to identify the relevant information for adeployed machine from the network accessible data storage facility. Toaddress commercial confidentiality concerns, some and/or allnetwork-accessible information may be protected by security measuressuch as passwords and the like. Similarly, information stored on anon-volatile storage facility, such as an RFID disposed with theindustrial machine, may include non-confidential information (e.g.,serial number, model number and the like) that may be accessible tothird-parties, and confidential information (e.g., performance data,last failure date, prediction of next failure, failure rate of themachine or sup-portion thereof, and the like) that may require explicitauthentication to access.

Accessing such rotating equipment information may include use of amobile data collector, such as a mobile phone equipped with a datacollection circuit that interacts with proximal industrial machines toaccess at least the non-confidential portion of the RFID tag. As thedata collection circuit is activated to communicate with industrialmachines, predictive maintenance beneficial information about theproximal industrial machines (e.g. as described herein and the like) maybe collected from the RFID directly or by apply indexing (e.g., URL andthe like) information gathered from the RFID to access the pertinentinformation from a networked server that is hosting the indexinginformation. In an example, a URL, which may be public data accessiblein the RFID and a serial number of the machine, which may be treated asconfidential information, may be retrieve from the RFID by the remotedata collector. The data collector may provide the retrieve informationto a predictive maintenance system that would apply the retrievedinformation in a web query to the URL, and the like.

Because some industrial machine deployments may not provide access toexternal networks like the Internet (e.g., for security purposes and thelike), information in the RFID may be gathered and applied to predictivemaintenance circuit operations contemporaneously with gathering theinformation; however predictive maintenance functions that requireinformation not available at the time of gathering (e.g., informationthat must be retrieved over the Internet) may be performed at a latertime, such as when the data collection circuit has access to theInternet and the like. In embodiments, predictive maintenance eventanalysis may be performed on a suitably equipped data collection device(e.g., a mobile device with sufficient processing power and datastorage, and the like) or on a server, such as a networked server andthe like, or a combination thereof. Predictive maintenance eventanalysis may also be performed by computing equipment that is accessibleover a network other than the Internet, such as a local area networkthat is accessible by the mobile data collector while in proximity tothe industrial machine(s). Such a site-specific local area network may,with proper credentials presented from the mobile data collector,facilitate access to industrial machine rotating part-relatedinformation over the Internet and the like.

In embodiments, rotor bar defects and weakening may be a precursor tosecondary deterioration that can lead to further and costly repairs,such as replacement of a rotor core and the like. Therefore, bydetecting broken or weakening rotor bars, maintenance and repair costsmay be minimized. Knowing the count of rotor bars may be a factor indetermining when maintenance and/or service of one or more rotor barsmay be best actioned. As an example, by applying a rotor bar failurerate to a formula that predicts when a rotor bar may fail, knowing acount of rotor bars for a given machine, among other things like cyclerate, age, and the like can facilitate predicting when conductingservice and/or testing of rotor bar-based systems could beneficially beconducted. A predictive maintenance circuit predicts maintenance eventsfor industrial and other machines may predict maintenance for a machinewith a greater number of rotor bars sooner than for a comparable machinewith fewer rotor bars.

In embodiments, predicting a maintenance event for a machine, such as arotating equipment-based machine may be adapted from a predictedmaintenance event for a similar machine while factoring in a count ofgear teeth in the machine and the similar machine. An aspect ofpredicting the maintenance event that may be affected by, for example acount of gear teeth, may be a timing of the event. In an example, amachine with a greater number of gear teeth relative to the similarmachine may suggest predicting a need for maintaining the machine withthe greater number of gear teeth sooner than the similar machine. Inembodiments, predicting a maintenance event for a moving part ofmachine, such as a rotating equipment-based part may be adapted from apredicted maintenance event for a similar part in the same or similarmachine while factoring in a count of gear teeth in the machine and thesimilar part or machine. In embodiments, predicting a maintenance eventfor a rotating part of machine, such as a rotating part of a rotatingequipment-based machine may be adapted from a predicted maintenanceevent for a similar rotating part in the same or similar machine whilefactoring in a count of gear teeth in the machine and the similar partor machine. In embodiments, predicting a maintenance event for a gearbox and the like, such as a rotating equipment-based gear box may beadapted from a predicted maintenance event for a similar part in thesame or similar machine while factoring in a count of gear teeth in themachine and the similar part or machine. In embodiments, predicting amaintenance event for a component of a machine comprising a multi-toothgear, such as a rotating equipment-based component may be adapted from apredicted maintenance event for a similar component in the same orsimilar machine while factoring in a count of gear teeth in the machineand the similar component or machine.

In embodiments, predicting a maintenance event for a rotating equipmentmay be a function of a predictive maintenance circuit that is, forexample, responsive to a count of gear teeth of a rotatable component ofa machine for which the predictive maintenance circuit products amaintenance event alert (e.g., a signal that facilitates triggering atleast an automated portion of a maintenance event, such as ordering areplacement part and the like). In embodiments, the predictivemaintenance circuit may process operational data for the machine orrotating portion thereof, and/or may process failure data for a specificrotating component and the like of the machine or similar machines;thereby incorporating contextual information about the specific machinewith static information about the machine such as gear teeth count andthe like in the prediction.

In embodiments, a count of gear teeth for a service component, such asfrom an RFID component integrated with or into an industrial machine,such as a rotary equipment, may be input to a machine learning circuitthat may process the input along with service information for similarservice components across a plurality of industrial machines. Themachine learning circuit may generate a predictive maintenanceadjustment factor that can be applied to the predictive maintenancecircuit processing thereby producing a machine-specific predictivemaintenance event.

In embodiments, predicting a maintenance event for a rotating equipmentmay be a function of a predictive maintenance circuit that is, forexample, responsive to a count of motor rotor bars of a rotatablecomponent of a machine for which the predictive maintenance circuitproducts a maintenance event alert. In embodiments, a count of motorrotor bars for a service component, such as from an RFID componentintegrated with or into an industrial machine, such as a rotaryequipment, may be input to a machine learning circuit that may processthe input along with service information for similar service componentsacross a plurality of industrial machines. The machine learning circuitmay generate a predictive maintenance adjustment factor that can beapplied to the predictive maintenance circuit processing therebyproducing a machine-specific predictive maintenance event.

In embodiments, predicting a maintenance event for a rotating equipmentmay be a function of a predictive maintenance circuit that is, forexample, responsive to data representative of a revolutions per minuteof, for example, an internal rotatable component of a machine for whichthe predictive maintenance circuit products a maintenance event alert.In embodiments, RPM data for a service component, such as from an RFIDcomponent integrated with or into an industrial machine, such as arotary equipment, may be input to a machine learning circuit that mayprocess the input along with service information for similar servicecomponents across a plurality of industrial machines. The machinelearning circuit may generate a predictive maintenance adjustment factorthat can be applied to the predictive maintenance circuit processingthereby producing a machine-specific predictive maintenance event.

In embodiments, predicting a maintenance event for a rotating equipmentmay be a function of a predictive maintenance circuit that is, forexample, responsive to data representative of an aspect of a rollerbearing, such as a number of balls per roller, a ball-to-roller contactangle, inner race dimensions, outer race dimensions, a number of vanes,a number of flutes, mode shape info, and the like of a rotatablecomponent of a machine for which the predictive maintenance circuitproducts a maintenance event alert. In embodiments, roller-bearingaspect data for a service component, such as from an RFID componentintegrated with or into an industrial machine, such as a rotaryequipment, may be input to a machine learning circuit that may processthe input along with service information for similar service componentsacross a plurality of industrial machines. The machine learning circuitmay generate a predictive maintenance adjustment factor that can beapplied to the predictive maintenance circuit processing therebyproducing a machine-specific predictive maintenance event. Inembodiments, a predicted maintenance event may be selected from a listof maintenance events including, without limitation part replacement,machine sub-system replacement, calibration, deep data collection,machine servicing, machine shutdown, preventive maintenance, and thelike.

In embodiments, at least one aspect of a roller bearing servicecomponent may be stored in a portion of digital data structure of rollerbearing component production information retrieved through an RFIDcomponent disposed with the roller bearing component into an industrialmachine. In embodiments, the portion of the digital data structure maybe specific to the industrial machine with which the roller bearingcomponent is disposed. In embodiments, the portion of the digital datastructure may be retrieved by accessing a network location retrievedfrom the RFID component and further indexed by a machine-specificidentifier retrieved from the RFID component. In embodiments, thenetwork location may be accessed through a WiFi interface of a datacollection device while the data collection device is in short rangewireless communication with the RFID component. Further in embodiments,the network location may be accessed through a WiFi interface of a datacollection device when the data collection device is no longer in shortrange wireless communication with the RFID component. In embodiments,the portion of the digital data structure may be retrieved by providinga machine-specific key retrieved from the RFID component to anApplication Programming Interface function of a predictive maintenancesystem that facilitates access to roller bearing component productioninformation stored external to the industrial machine. In embodiments,the portion of the digital data structure may include productioninformation retrieved from the RFID component. In embodiments, thecircuit predicts a maintenance event for the roller bearing componentresponsive to retrieving the portion of the digital data structure fromthe RFID component independent of network connectivity of a processorexecuting the circuit. Yet further in embodiments, a data collectiondevice may include the predictive maintenance circuit that predicts amaintenance event for the roller bearing component responsive toretrieving the portion of the digital data structure from the RFIDcomponent independent of network connectivity of the data collectiondevice.

Referring to FIG. 326, a diagram of a data structure 31000 for storingrotating part-related information for use in, among other things,predicting a maintenance event for a portion of an industrial machineassociated with the rotating part is depicted. A rotating component31002 may include a specific gear of an industrial machine, a gear in agearbox, a shaft, roller bearings and the like. Parameters 31004 foreach rotating component may include, without limitation, count of teeth,count of gears, type(s) of gears in a gear box, rotation rate, count ofballs, race dimensions, number of vanes and the like. Values 31006 foreach rotating component-parameter combination may be stored in the datastructure 31000. This data structure maybe representative of a portionof rotating part data stored on an RFID component deployed with anindustrial machine. The number of entries on the data structure, typesof data in the data structure, and formats for values (e.g., decimal,hexadecimal, and the like) may vary as needed to support storingrotating part-related configuration, production and test information.

Referring to FIG. 327, a flow chart is depicted that represents a methodfor predicting a maintenance event for a rotating part, such as a gear,motor, roller bearing and the like based on as stream of sensed rotatingpart health data and part-specific configuration information, such asgear tooth count, roller bearing/chase dimensions, rotor bar count for amotor, and the like. A method 31100 may include a step 31102 ofgenerating streams of health data for a rotating part, such as a gear,motor, roller bearing and the like. The method 31100 may continue with astep 31104 of accessing configuration information for the rotating part,such as from an RFID part deployed with the industrial machine hostingthe rotating part and/or from a network-accessible data storagefacility. The method 31100 may continue with a step 31106 of predictingat least one of a gear, motor, and/or roller bearing related maintenanceevent/action/likelihood. The method 31100 may continue with a step 31108of producing orders for the predicted maintenance action to maintain,repair, and/or replace the rotating part for which a maintenanceaction/event is predicted. The method 31100 may continue with a step31110 of validating the maintenance action(s) taken based on therotating part based on service data for the maintenance event; such datafor the maintenance event may be received by a processor, such as anetworked server from the industrial machine and the like.

While the many features disclosed herein may be described independent ofone another, it should be understood that combinations of those featuresare possible in various embodiments. In embodiments, such combinationsmay refer to or include combinations of two or more of: the use ofmobile data collectors, for example, wearable devices, handheld devices,mobile robots, and/or mobile vehicles; the use of ledgers, for example,with a blockchain structure, to store records related to predictivemaintenance of industrial machines; converting or mapping vibration datato severity units; or predictive maintenance of industrial machines. Itshould be understood that other combinations of features not explicitlystated in combination herein are possible in accordance with theembodiments of this disclosure.

While the foregoing written description enables one skilled in the artto make and use what is considered presently to be the best modethereof, those skilled in the art will understand and appreciate theexistence of variations, combinations, and equivalents of the specificembodiment, method, and examples herein. The disclosure should thereforenot be limited by the above described embodiment, method, and examples,but by all embodiments and methods within the scope and spirit of thedisclosure.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions, and thelike. The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor, or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor, and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon.

In addition, the processor may enable execution of multiple programs,threads, and codes. The threads may be executed simultaneously toenhance the performance of the processor and to facilitate simultaneousoperations of the application. By way of implementation, methods,program codes, program instructions, and the like described herein maybe implemented in one or more thread. The thread may spawn other threadsthat may have assigned priorities associated with them; the processormay execute these threads based on priority or any other order based oninstructions provided in the program code. The processor, or any machineutilizing one, may include non-transitory memory that stores methods,codes, instructions, and programs as described herein and elsewhere. Theprocessor may access a non-transitory storage medium through aninterface that may store methods, codes, and instructions as describedherein and elsewhere. The storage medium associated with the processorfor storing methods, programs, codes, program instructions, or othertype of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server, and the like. Theserver may include one or more of memories, processors, computerreadable transitory and/or non-transitory media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other servers, clients, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the server. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationswithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, code,and/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient, and other variants such as secondary client, host client,distributed client, and the like. The client may include one or more ofmemories, processors, computer readable transitory and/or non-transitorymedia, storage media, ports (physical and virtual), communicationdevices, and interfaces capable of accessing other clients, servers,machines, and devices through a wired or a wireless medium, and thelike. The methods, programs, or codes as described herein and elsewheremay be executed by the client. In addition, other devices required forexecution of methods as described in this application may be consideredas a part of the infrastructure associated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of a program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code, and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

In embodiments, one or more of the controllers, circuits, systems, datacollectors, storage systems, network elements, components, or the likeas described throughout this disclosure may be embodied in or on anintegrated circuit, such as an analog, digital, or mixed signal circuit,such as a microprocessor, a programmable logic controller, anapplication-specific integrated circuit, a field programmable gatearray, or other circuit, such as embodied on one or more chips disposedon one or more circuit boards, such as to provide in hardware (withpotentially accelerated speed, energy performance, input-outputperformance, or the like) one or more of the functions described herein.This may include setting up circuits with up to billions of logic gates,flip-flops, multiplexers, and other circuits in a small space,facilitating high speed processing, low power dissipation, and reducedmanufacturing cost compared with board-level integration. Inembodiments, a digital IC, typically a microprocessor, digital signalprocessor, microcontroller, or the like may use Boolean algebra toprocess digital signals to embody complex logic, such as involved in thecircuits, controllers, and other systems described herein. Inembodiments, a data collector, an expert system, a storage system, orthe like may be embodied as a digital integrated circuit (“IC”), such asa logic IC, memory chip, interface IC (e.g., a level shifter, aserializer, a deserializer, and the like), a power management IC and/ora programmable device; an analog integrated circuit, such as a linearIC, RF IC, or the like, or a mixed signal IC, such as a data acquisitionIC (including A/D converters, D/A converter, digital potentiometers)and/or a clock/timing IC.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM, and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be configured for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (“SaaS”), platformas a service (“PaaS”), and/or infrastructure as a service (“IaaS”).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (“FDMA”) network or code division multiple access (“CDMA”)network. The cellular network may include mobile devices, cell sites,base stations, repeaters, antennas, towers, and the like. The cellnetwork may be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable transitory and/or non-transitorymedia that may include: computer components, devices, and recordingmedia that retain digital data used for computing for some interval oftime; semiconductor storage known as random access memory (“RAM”); massstorage typically for more permanent storage, such as optical discs,forms of magnetic storage like hard disks, tapes, drums, cards and othertypes; processor registers, cache memory, volatile memory, non-volatilememory; optical storage such as CD, DVD; removable media such as flashmemory (e.g., USB sticks or keys), floppy disks, magnetic tape, papertape, punch cards, standalone RAM disks, zip drives, removable massstorage, off-line, and the like; other computer memory such as dynamicmemory, static memory, read/write storage, mutable storage, read only,random access, sequential access, location addressable, fileaddressable, content addressable, network attached storage, storage areanetwork, bar codes, magnetic ink, and the like.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the Figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable transitory and/ornon-transitory media having a processor capable of executing programinstructions stored thereon as a monolithic software structure, asstandalone software modules, or as modules that employ externalroutines, code, services, and so forth, or any combination of these, andall such implementations may be within the scope of the presentdisclosure. Examples of such machines may include, but may not belimited to, personal digital assistants, laptops, personal computers,mobile phones, other handheld computing devices, medical equipment,wired or wireless communication devices, transducers, chips,calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers, and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar references inthe context of describing the disclosure (especially in the context ofthe following claims) is to be construed to cover both the singular andthe plural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. All methodsdescribed herein can be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the disclosure,and does not pose a limitation on the scope of the disclosure unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe disclosure.

Implementations of approaches described above may include softwareimplementations, which use software instructions stored onnon-transitory machine-readable media. The procedures and protocols asdescribed above in the text and figures are sufficient for one skilledin the art to implement them in such software implementations. In someexamples, the software may execute on a client node (e.g., a smartphone)using a general-purpose processor that implements a variety of functionson the client node. Software that executes on end nodes or intermediatenetwork nodes may use processors that are dedicated to processingnetwork traffic, for example, being embedded in network processingdevices. In some implementations, certain functions may be implementedin hardware, for example, using Application-Specific Integrated Circuits(ASICs), and/or FPGAs, thereby reducing the load on a general purposeprocessor.

Note that in some diagrams and figures in this disclosure, networks suchas the internet, carrier networks, internet service provider networks,LANs, metro area networks (MANs), WANs, storage area networks (SANs),backhaul networks, cellular networks, satellite networks and the like,may be depicted as clouds. Also note, that certain processes may bereferred to as taking place in the cloud and devices may be described asaccessing the cloud. In these types of descriptions, the cloud should beunderstood to be some type of network comprising networking equipmentand wireless and/or wired links.

The description above may refer to a client device communicating with aserver, but it should be understood that the technology and techniquesdescribed herein are not limited to those exemplary devices as theend-points of communication connections or sessions. The end-points mayalso be referred to as, or may be, senders, transmitters, transceivers,receivers, servers, video servers, content servers, proxy servers, cloudstorage units, caches, routers, switches, buffers, mobile devices,tablets, smart phones, handsets, computers, set-top boxes, modems,gaming systems, nodes, satellites, base stations, gateways, satelliteground stations, wireless access points, and the like. The devices atany of the end-points or intermediate nodes of communication connectionsor sessions may be commercial media streaming boxes such as thoseimplementing Apple TV, Roku, Chromecast, Amazon Fire, Slingbox, and thelike, or they may be custom media streaming boxes. The devices at theany of the end-points or intermediate nodes of communication connectionsor sessions may be smart televisions and/or displays, smart appliancessuch as hubs, refrigerators, security systems, power panels and thelike, smart vehicles such as cars, boats, busses, trains, planes, carts,and the like, and may be any device on the IoT. The devices at any ofthe end-points or intermediate nodes of communication connections orsessions may be single-board computers and/or purpose built computingengines comprising processors such as ARM processors, video processors,system-on-a-chip (SoC), and/or memory such as random access memory(RAM), read only memory (ROM), or any kind of electronic memorycomponents.

Communication connections or sessions may exist between two routers, twoclients, two network nodes, two servers, two mobile devices, and thelike, or any combination of potential nodes and/or end-point devices. Inmany cases, communication sessions are bi-directional so that bothend-point devices may have the ability to send and receive data. Whilethese variations may not be stated explicitly in every description andexemplary embodiment in this disclosure, it should be understood thatthe technology and techniques we describe herein are intended to beapplied to all types of known end-devices, network nodes and equipmentand transmission links, as well as to future end-devices, network nodesand equipment and transmission links with similar or improvedperformance.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platforms. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions, and thelike. The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor, or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor, and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions, and programs as described hereinand elsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server, and the like. Theserver may include one or more of memories, processors, computerreadable transitory and/or non-transitory media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other servers, clients, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the server. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationwithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, codeand/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client, and the like. The client may include one or more ofmemories, processors, computer readable transitory and/or non-transitorymedia, storage media, ports (physical and virtual), communicationdevices, and interfaces capable of accessing other clients, servers,machines, and devices through a wired or a wireless medium, and thelike. The methods, programs, or codes as described herein and elsewheremay be executed by the client. In addition, other devices required forexecution of methods as described in this application may be consideredas a part of the infrastructure associated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM, and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be adapted for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (“SaaS”), platformas a service (“PaaS”), and/or infrastructure as a service (“IaaS”).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (“FDMA”) network or code division multiple access (“CDMA”)network. The cellular network may include mobile devices, cell sites,base stations, repeaters, antennas, towers, and the like. The cellnetwork may be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable transitory and/or non-transitorymedia that may include: computer components, devices, and recordingmedia that retain digital data used for computing for some interval oftime; semiconductor storage known as random access memory (“RAM”); massstorage typically for more permanent storage, such as optical discs,forms of magnetic storage like hard disks, tapes, drums, cards and othertypes; processor registers, cache memory, volatile memory, non-volatilememory; optical storage such as CD, DVD; removable media such as flashmemory (e.g., USB sticks or keys), floppy disks, magnetic tape, papertape, punch cards, standalone RAM disks, zip drives, removable massstorage, off-line, and the like; other computer memory such as dynamicmemory, static memory, read/write storage, mutable storage, read only,random access, sequential access, location addressable, fileaddressable, content addressable, network attached storage, storage areanetwork, bar codes, magnetic ink, and the like.

The methods and systems described herein may transform physical and/orintangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable transitory and/ornon-transitory media having a processor capable of executing programinstructions stored thereon as a monolithic software structure, asstandalone software modules, or as modules that employ externalroutines, code, services, and so forth, or any combination of these, andall such implementations may be within the scope of the presentdisclosure. Examples of such machines may include, but may not belimited to, personal digital assistants, laptops, personal computers,mobile phones, other handheld computing devices, medical equipment,wired or wireless communication devices, transducers, chips,calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers, and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable devices, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

The use of the terms “a,” “an,” and “the” and similar references in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitations of ranges ofvalues herein are merely intended to serve as a shorthand method ofreferring individually to each separate value falling within the range,unless otherwise indicated herein, and each separate value isincorporated into the specification as if it were individually recitedherein. All methods described herein may be performed in any suitableorder unless otherwise indicated herein or otherwise clearlycontradicted by context. The use of any and all examples, or exemplarylanguage (e.g., “such as”) provided herein, is intended merely to betterilluminate the disclosure and does not pose a limitation on the scope ofthe disclosure unless otherwise claimed. No language in thespecification should be construed as indicating any non-claimed elementas essential to the practice of the disclosure.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specifiedfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 U.S.C. § 112(f). In particular, any use of “step of” inthe claims is not intended to invoke the provision of 35 U.S.C. §112(f).

Persons of skilled in the art may appreciate that numerous designconfigurations may be possible to enjoy the functional benefits of theinventive systems. Thus, given the wide variety of configurations andarrangements of embodiments of the present invention the scope of theinvention is reflected by the breadth of the claims below rather thannarrowed by the embodiments described above.

It is to be understood that the foregoing description is intended toillustrate and not to limit the scope of the invention, some aspects ofwhich are defined by the scope of the appended claims. Furthermore,other embodiments are within the scope of the following claims.

What is claimed is:
 1. A system for predicting a service event fromvibration data, comprising: an industrial machine comprising at leastone vibration sensor disposed to capture vibration of a portion of theindustrial machine; a vibration analysis circuit in communication withthe at least one vibration sensor and that generates at least one of afrequency, peak amplitude, and gravitational force of the capturedvibration; a multi-segment vibration frequency spectra structure thatfacilitates mapping the captured vibration to one vibration frequencysegment of a multi-segment vibration frequency; a severity unitalgorithm that receives the frequency of the captured vibration and thecorresponding vibration frequency segment and produces a severity valuewhich is then mapped to one of a plurality of severity units defined forthe corresponding vibration frequency segment; and a signal generatingcircuit that receives the one of the plurality of severity units, andbased thereon, signals a predictive maintenance server to execute acorresponding maintenance action on the portion of the industrialmachine.
 2. The system of claim 1, wherein the multi-segment vibrationfrequency spectra structure facilitates a mapping of the detectedvibrations to a first severity unit when the frequency of the capturedvibration corresponds to a below a low-end knee threshold-range of themulti-segment vibration frequency spectra.
 3. The system of claim 1,wherein the multi-segment vibration frequency spectra structurefacilitates a mapping of the detected vibrations to a second severityunit when the frequency of the captured vibration corresponds to amid-range of the multi-segment vibration frequency spectra.
 4. Thesystem of claim 1, wherein the multi-segment vibration frequency spectrastructure facilitates a mapping of the detected vibrations to a thirdseverity unit when the frequency of the captured vibration correspondsto an above a high-end knee threshold-range of the multi-segmentvibration frequency spectra.
 5. The system of claim 1, wherein theseverity units indicate that the detected vibrations may lead to afailure of at least the portion of the industrial machine.
 6. The systemof claim 1, wherein a first segment of the multi-segment vibrationfrequency spectra is divided into a plurality of severity units based onthe amplitude of the captured vibration.
 7. The system of claim 1,wherein a second segment of the multi-segment vibration frequencyspectra is divided into a plurality of severity units based on thegravitational force of the captured vibration.
 8. The system of claim 1,wherein the severity unit is determined based on a peak displacement ofthe amplitude of the captured vibration for determined vibrationfrequencies within a first segment of the multi-segment vibrationfrequency spectra.
 9. The system of claim 1, wherein the severity unitis determined based on gravitational force of the captured vibration fordetermined vibration frequencies within a second segment of themulti-segment vibration frequency spectra.
 10. The system of claim 1,wherein the portion of the industrial machine is a moving part.
 11. Thesystem of claim 1, wherein the portion of the industrial machine is astructural member supporting a moving part.
 12. The system of claim 1,wherein the portion of the industrial machine is a motor.
 13. The systemof claim 1, wherein the portion of the industrial machine is a driveshaft.
 14. A method comprising: sampling a signal at a streaming samplerate, thereby producing a plurality of samples of the signal;allocating, with a signal routing circuit, a first portion of theplurality of samples of the signal to a first signal analysis circuit,the portion selected based on a first signal analysis sampling rate thatis less than the streaming sample rate; allocating, with a signalrouting circuit, a second portion of the plurality of samples of thesignal to a second signal analysis circuit, the portion selected basedon a second signal analysis sampling rate that is less than thestreaming sample rate; and storing the plurality of samples of thesignal, an output of the first signal analysis circuit, and an output ofthe second signal analysis circuit, wherein the allocated first portionin the stored plurality of samples and the allocated second portion inthe stored plurality of samples are tagged with indicia that referencesthe corresponding stored signal analysis output.
 15. The method of claim14, wherein allocating with the signal routing circuit comprisesintegrating a plurality of samples based on a ratio of the signalanalysis sampling rate and the streaming sample rate.
 16. The method ofclaim 14 wherein allocating with the signal routing circuit comprisesselecting samples of the signal based on a ratio of the signal analysissampling rate and the streaming sample rate.
 17. The method of claim 14,wherein the streaming sample rate is at least twice as fast as adominant frequency of the signal.
 18. The method of claim 14, wherein aratio of the signal analysis sampling rate to the streaming sample ratedetermines a number of supplemental binary bits of data of the output ofthe first and second signal analysis circuits.
 19. The method of claim18, wherein the number of supplemental binary bits comprises one whenthe streaming sample rate is at least twice and less than four times thesignal analysis sampling rate.
 20. The method of claim 18, wherein thenumber of supplemental binary bits comprise two when the streamingsample rate is at least four times and less than eight times the signalanalysis sampling rate.